{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee58fdba",
   "metadata": {},
   "source": [
    "### genshin project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d6b4de-5aef-4a7e-be95-df2c1ed72ed1",
   "metadata": {},
   "source": [
    "### Import Required Libraries\n",
    "\n",
    "This cell imports all the necessary Python libraries used in the analysis:\n",
    "\n",
    "- `os` and `os.path`: For file and directory operations.\n",
    "- `pandas`: For data manipulation and DataFrame operations.\n",
    "- `numpy`: For numerical computations and array handling.\n",
    "- `datetime`: For parsing and manipulating time-related data.\n",
    "- `tqdm`: For displaying progress bars in loops.\n",
    "- `math`: For mathematical operations.\n",
    "- `matplotlib.pyplot`: For plotting and visualizing data.\n",
    "- `collections.defaultdict`: For convenient default dictionary creation.\n",
    "- `scipy.stats.chisquare`: For performing the chi-square test in statistical analysis.\n",
    "- `random`: For random sampling, used in permutation testing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "07648480-9292-48d3-a450-066129244dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os.path as osp\n",
    "import datetime\n",
    "import tqdm\n",
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import defaultdict\n",
    "from scipy.stats import chisquare\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7656702-9cbb-4293-a4ab-ca12712c8dbd",
   "metadata": {},
   "source": [
    "### Define Data Directory and File\n",
    "\n",
    "This section specifies the location of the dataset and selects the file used for analysis:\n",
    "\n",
    "- `data_dir`: Sets the path to the directory containing the gacha dataset.\n",
    "- `os.listdir(data_dir)`: Lists all files in the specified directory (useful for confirming available files).\n",
    "- `pool_file`: Specifies the target file for analysis — `gacha301.csv`, which contains draw records specifically from the **character pool**. \n",
    "  This is consistent with the experimental design that excludes the weapon pool due to additional randomness introduced by the \"Epitomized Path\" system.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b497d371-721f-4381-8f7e-4a08fcae2e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data directory\n",
    "data_dir = \"../data/GI_gacha_dataset_02\"\n",
    "files = os.listdir(data_dir)\n",
    "# Filename definition (only character pool)\n",
    "pool_file = 'gacha301.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb413111-fca2-492a-abde-75a079fd1ad9",
   "metadata": {},
   "source": [
    "### Step 1: Extract Non-Pity Pulls from Character Pool\n",
    "\n",
    "This block iterates over all user folders and collects relevant draw records from the character pool (`gacha301.csv`). The objective is to filter out non-pity five-star pulls — that is, five-star results occurring before the soft-pity threshold (73 pulls).\n",
    "\n",
    "**Key operations:**\n",
    "- For each user:\n",
    "  - Load their character pool draw history.\n",
    "  - Parse the draw time and star rating of each pull.\n",
    "  - Track the number of pulls since the last five-star item.\n",
    "  - If the count is less than 73, record the pull as a non-pity attempt, saving:\n",
    "    - Exact time,\n",
    "    - Star rating,\n",
    "    - Hour of the day,\n",
    "    - 10-minute group within the hour (`minute // 10`),\n",
    "    - Last digit of the seconds (`second % 10`).\n",
    "\n",
    "These features will be used for later time-based groupings.\n",
    "\n",
    "Note: If a five-star is pulled, the `pull_since_last_5` counter resets. Pulls after 73 are ignored due to the increasing \"soft-pity\" probability.\n",
    "#### Note on the 73-Pull Threshold\n",
    "\n",
    "The threshold of 73 pulls is based on empirical community research on Genshin Impact's gacha system. According to player-collected statistics and the explanatory video from OneBST (https://www.bilibili.com/opus/467414504782366723/), the game introduces a \"soft pity\" system around the 74th pull. Prior to this point, the probability of pulling a five-star item remains relatively stable at approximately 0.6%.\n",
    "\n",
    "Therefore, to ensure that the observed data reflect a constant probability model, only the first 73 pulls in each pity cycle are considered \"non-pity\" pulls in this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34822e1a-8de8-4ca0-8ff0-e4118580c676",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 175/175 [00:08<00:00, 21.36it/s]\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Collect all non-pity pulls from character pool\n",
    "all_non_pity_attempts = []  # Stores each pull with time, star, and extracted time components\n",
    "for user_id in tqdm.tqdm(files):\n",
    "    user_path = osp.join(data_dir, user_id)\n",
    "    if not osp.isdir(user_path):\n",
    "        continue\n",
    "\n",
    "    file_path = osp.join(user_path, pool_file)\n",
    "    if not osp.exists(file_path):\n",
    "        continue\n",
    "\n",
    "    try:\n",
    "        df = pd.read_csv(file_path)\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "    pull_since_last_5 = 0\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        try:\n",
    "            time_dt = datetime.datetime.strptime(row['抽卡时间'], '%Y-%m-%d %H:%M:%S')\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "        star = row['星级']\n",
    "\n",
    "        if pull_since_last_5 < 73:\n",
    "            all_non_pity_attempts.append({\n",
    "                'time': time_dt,\n",
    "                'star': star,\n",
    "                'hour': time_dt.hour,\n",
    "                'minute_group': time_dt.minute // 10,\n",
    "                'second_digit': time_dt.second % 10\n",
    "            })\n",
    "\n",
    "        pull_since_last_5 += 1\n",
    "\n",
    "        if star == 5:\n",
    "            if pull_since_last_5 < 73:\n",
    "                pass\n",
    "            pull_since_last_5 = 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29dd6c68-df0e-414a-aadb-4f5e6ee3ad31",
   "metadata": {},
   "source": [
    "### Step 2: Compute Global Non-Pity Hit Rate\n",
    "\n",
    "This block calculates the global five-star hit rate for non-pity pulls across all users in the character pool:\n",
    "\n",
    "- `total_attempts`: Total number of non-pity draw attempts (i.e., draws made before the 73-pull threshold in each pity cycle).\n",
    "- `total_hits`: Number of those attempts that resulted in a five-star item.\n",
    "- `global_hit_rate`: Overall five-star success rate among non-pity attempts.\n",
    "\n",
    "This global hit rate serves as the expected baseline for later statistical comparisons across different time intervals. \n",
    "\n",
    "Note: In Genshin Impact, the official non-pity hit rate for the character pool is approximately 0.6%, and this empirical value will be compared against group-specific hit rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2007ccf0-9bc6-45f1-9c85-0b10ed4d23d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_attempts (character pool): 112476.000000\n",
      "total_hits (character pool): 707.000000\n",
      "Global non-pity hit rate (character pool): 0.006286\n"
     ]
    }
   ],
   "source": [
    "# Compute global non-pity hit rate\n",
    "total_attempts = len(all_non_pity_attempts)\n",
    "total_hits = sum(1 for x in all_non_pity_attempts if x['star'] == 5)\n",
    "global_hit_rate = total_hits / total_attempts if total_attempts > 0 else 0\n",
    "print(f\"total_attempts (character pool): {total_attempts:.6f}\")\n",
    "print(f\"total_hits (character pool): {total_hits:.6f}\")\n",
    "print(f\"Global non-pity hit rate (character pool): {global_hit_rate:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947b1ce7-f625-40f8-9865-1d53f6fd20b6",
   "metadata": {},
   "source": [
    "### Global Non-Pity Hit Rate Evaluation\n",
    "\n",
    "Based on a total of **112,476** non-pity draw attempts from the character pool, **707** resulted in five-star items. This yields a global non-pity five-star hit rate of approximately **0.6286%**, which is highly consistent with the official drop rate of **0.6%** reported by the game developer.\n",
    "\n",
    "This empirical result confirms the community's understanding of the game's gacha mechanics, specifically that the five-star probability remains stable and approximately constant during the first 73 pulls of each pity cycle (i.e., before soft pity effects take hold).\n",
    "\n",
    "The derived global hit rate will serve as a reference baseline in the subsequent statistical analysis to assess whether hit rates deviate significantly across different time groupings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2968ff-8106-457e-bf74-379af98a98fa",
   "metadata": {},
   "source": [
    "### Classification and Statistical Analysis Function\n",
    "\n",
    "The function `analyze_by_key()` performs grouped statistical analysis of non-pity five-star hit rates by different time-based keys (e.g., hour, 10-minute block, or second digit).\n",
    "\n",
    "#### Function Parameters:\n",
    "- `key_name`: The field in `all_non_pity_attempts` used for grouping (e.g., `'hour'`, `'minute_group'`, or `'second_digit'`).\n",
    "- `num_groups`: Number of groups to expect (e.g., 24 for hours, 6 for 10-minute blocks, 10 for second digits).\n",
    "- `label_fmt`: A string label to be used in plot axes (e.g., \"Hour of Day\", \"10-Minute Block\").\n",
    "\n",
    "#### Analysis Steps:\n",
    "1. **Group Aggregation**:\n",
    "   - Counts the number of attempts and five-star hits in each group.\n",
    "   - Calculates the hit rate (`hits / attempts`) and standard error using the global hit rate.\n",
    "\n",
    "2. **DataFrame Construction**:\n",
    "   - Constructs a summary table including: group ID, attempts, hits, hit rate, and standard error.\n",
    "\n",
    "3. **Visualization**:\n",
    "   - Plots a bar chart of hit rates with error bars representing standard error.\n",
    "   - Plots a second chart showing the number of attempts per group (to assess sample size distribution).\n",
    "\n",
    "4. **Chi-Square Test**:\n",
    "   - Compares observed five-star hits against expected values (based on global rate).\n",
    "   - Prints test statistics and significance interpretation.\n",
    "\n",
    "This function provides a complete per-group breakdown of non-pity five-star hit rate distributions and evaluates whether differences across time groups are statistically significant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b0d68df-9ce7-4df7-a32b-b7e6cd713b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ========== Classification and Analysis Functions ==========\n",
    "def analyze_by_key(key_name, num_groups, label_fmt):\n",
    "    counter = defaultdict(int)\n",
    "    hit_counter = defaultdict(int)\n",
    "\n",
    "    for entry in all_non_pity_attempts:\n",
    "        key = entry[key_name]\n",
    "        counter[key] += 1\n",
    "        if entry['star'] == 5:\n",
    "            hit_counter[key] += 1\n",
    "\n",
    "    hit_rate = {}\n",
    "    se = {}\n",
    "    for k in range(num_groups):\n",
    "        total = counter[k]\n",
    "        hits = hit_counter[k]\n",
    "        rate = hits / total if total > 0 else 0\n",
    "        stderr = math.sqrt(global_hit_rate * (1 - global_hit_rate) / total) if total > 0 else 0\n",
    "        hit_rate[k] = rate\n",
    "        se[k] = stderr\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "        'group': list(hit_rate.keys()),\n",
    "        'attempts': [counter[k] for k in hit_rate],\n",
    "        'hits': [hit_counter[k] for k in hit_rate],\n",
    "        'hit_rate': [hit_rate[k] for k in hit_rate],\n",
    "        'se': [se[k] for k in hit_rate]\n",
    "    })\n",
    "\n",
    "    print(f\"\\n=== Hit Rate by {key_name} ===\")\n",
    "    print(df)\n",
    "\n",
    "    # Plot hit rate with error bars\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.bar(df['group'], df['hit_rate'], yerr=df['se'], capsize=5, color='steelblue')\n",
    "    plt.title(f'Non-pity 5-star Hit Rate by {key_name}')\n",
    "    plt.xlabel(f'{key_name} ({label_fmt})')\n",
    "    plt.ylabel('Hit Rate')\n",
    "    plt.grid(axis='y')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Plot attempts per group\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.bar(df['group'], df['attempts'], color='lightgray')\n",
    "    plt.title(f'Attempts by {key_name}')\n",
    "    plt.xlabel(f'{key_name} ({label_fmt})')\n",
    "    plt.ylabel('Number of Attempts')\n",
    "    plt.grid(axis='y')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Chi-squared test\n",
    "    observed = np.array([hit_counter[k] for k in range(num_groups)])\n",
    "    expected = np.array([counter[k] * global_hit_rate for k in range(num_groups)])\n",
    "    chi2_stat, p_val = chisquare(f_obs=observed, f_exp=expected)\n",
    "    print(f\"\\nChi-squared test for {key_name}: Chi2 = {chi2_stat:.4f}, p = {p_val:.4f}\")\n",
    "    if p_val < 0.05:\n",
    "        print(\"Result is statistically significant: Differences may not be due to chance.\")\n",
    "    else:\n",
    "        print(\"Result is not statistically significant: Differences may be due to natural variation.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6e63b7-98dc-4b7d-977d-cd52becca5f5",
   "metadata": {},
   "source": [
    "### Run Hit Rate Analysis Across Multiple Time-Based Groupings\n",
    "\n",
    "This section executes the `analyze_by_key()` function for four different time-based classification schemes. The goal is to explore whether the non-pity five-star hit rate varies significantly across different time intervals.\n",
    "\n",
    "#### 1. By Hour (0–23):\n",
    "- Groups draws by the hour of the day.\n",
    "- Tests whether hit rate shows variation over 24-hour cycle (e.g., \"lucky hours\").\n",
    "\n",
    "#### 2. By 6-Hour Time Block:\n",
    "- Aggregates hours into four broader blocks: \n",
    "  - Block 0: 0–5h\n",
    "  - Block 1: 6–11h\n",
    "  - Block 2: 12–17h\n",
    "  - Block 3: 18–23h\n",
    "- This reduces noise from low-sample hours and helps stabilize statistical power.\n",
    "\n",
    "#### 3. By Minute Group (within an hour):\n",
    "- Divides each hour into six 10-minute intervals (e.g., 0–9, 10–19, …, 50–59).\n",
    "- Useful for detecting finer-grained superstition patterns (e.g., \"draw at xx:10\").\n",
    "\n",
    "#### 4. By Last Digit of Second:\n",
    "- Classifies based on the last digit of the second (0–9).\n",
    "- Designed to test extremely fine-grained myths like \"hit better on xx:xx:xx9\".\n",
    "\n",
    "Each analysis produces:\n",
    "- A bar chart of hit rates with standard error bars;\n",
    "- A count plot of attempts per group;\n",
    "- Chi-squared test to assess statistical significance of observed hit rate variation.\n",
    "\n",
    "These experiments provide a comprehensive view of whether temporal patterns affect the fairness of the gacha system.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb9dab8f-5037-4541-aa4a-6dea9c70c9a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Hit Rate by hour ===\n",
      "    group  attempts  hits  hit_rate        se\n",
      "0       0      6160    30  0.004870  0.001007\n",
      "1       1      2700    25  0.009259  0.001521\n",
      "2       2      3028    18  0.005945  0.001436\n",
      "3       3      1203     9  0.007481  0.002279\n",
      "4       4      1604    11  0.006858  0.001973\n",
      "5       5       744     6  0.008065  0.002898\n",
      "6       6      1266     6  0.004739  0.002221\n",
      "7       7       901     7  0.007769  0.002633\n",
      "8       8      1787     7  0.003917  0.001870\n",
      "9       9      6969    38  0.005453  0.000947\n",
      "10     10      7083    53  0.007483  0.000939\n",
      "11     11      6704    50  0.007458  0.000965\n",
      "12     12      5672    34  0.005994  0.001049\n",
      "13     13      4177    20  0.004788  0.001223\n",
      "14     14      3152    23  0.007297  0.001408\n",
      "15     15      3275    27  0.008244  0.001381\n",
      "16     16      4574    25  0.005466  0.001169\n",
      "17     17      3323    19  0.005718  0.001371\n",
      "18     18     17137   106  0.006185  0.000604\n",
      "19     19      7675    58  0.007557  0.000902\n",
      "20     20      5752    33  0.005737  0.001042\n",
      "21     21      6625    42  0.006340  0.000971\n",
      "22     22      6150    31  0.005041  0.001008\n",
      "23     23      4815    29  0.006023  0.001139\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Chi-squared test for hour: Chi2 = 21.3785, p = 0.5580\n",
      "Result is not statistically significant: Differences may be due to natural variation.\n",
      "\n",
      "=== Hit Rate by hour_block ===\n",
      "   group  attempts  hits  hit_rate        se\n",
      "0      0     15439    99  0.006412  0.000636\n",
      "1      1     24710   161  0.006516  0.000503\n",
      "2      2     24173   148  0.006123  0.000508\n",
      "3      3     48154   299  0.006209  0.000360\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Chi-squared test for hour_block: Chi2 = 0.3943, p = 0.9414\n",
      "Result is not statistically significant: Differences may be due to natural variation.\n",
      "\n",
      "=== Hit Rate by minute_group ===\n",
      "   group  attempts  hits  hit_rate        se\n",
      "0      0     22811   142  0.006225  0.000523\n",
      "1      1     18822   114  0.006057  0.000576\n",
      "2      2     18460   137  0.007421  0.000582\n",
      "3      3     16191   110  0.006794  0.000621\n",
      "4      4     18382   104  0.005658  0.000583\n",
      "5      5     17810   100  0.005615  0.000592\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Chi-squared test for minute_group: Chi2 = 7.0523, p = 0.2168\n",
      "Result is not statistically significant: Differences may be due to natural variation.\n",
      "\n",
      "=== Hit Rate by second_digit ===\n",
      "   group  attempts  hits  hit_rate        se\n",
      "0      0     11605    73  0.006290  0.000734\n",
      "1      1     11780    82  0.006961  0.000728\n",
      "2      2     11115    71  0.006388  0.000750\n",
      "3      3     10996    75  0.006821  0.000754\n",
      "4      4     11129    76  0.006829  0.000749\n",
      "5      5     11290    65  0.005757  0.000744\n",
      "6      6     10777    62  0.005753  0.000761\n",
      "7      7     11913    75  0.006296  0.000724\n",
      "8      8     10889    64  0.005877  0.000757\n",
      "9      9     10982    64  0.005828  0.000754\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Chi-squared test for second_digit: Chi2 = 3.5395, p = 0.9390\n",
      "Result is not statistically significant: Differences may be due to natural variation.\n"
     ]
    }
   ],
   "source": [
    "# ========== Run Multiple Classifications ==========\n",
    "\n",
    "# 1. Hour (0-23)\n",
    "analyze_by_key('hour', 24, '0-23')\n",
    "\n",
    "# 2. 6-hour time block (0: 0-5, 1: 6-11, ...)\n",
    "for entry in all_non_pity_attempts:\n",
    "    entry['hour_block'] = entry['hour'] // 6\n",
    "analyze_by_key('hour_block', 4, '4 groups of 6 hours')\n",
    "\n",
    "# 3. Minute group (6 bins: 0-9, 10-19, ..., 50-59)\n",
    "analyze_by_key('minute_group', 6, 'each 10-minute bin')\n",
    "\n",
    "# 4. Second digit (last digit of second: 0-9)\n",
    "analyze_by_key('second_digit', 10, 'last digit of second')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1450f3-34cb-4ae2-b3bc-d378b83a6000",
   "metadata": {},
   "source": [
    "### Summary of Temporal Hit Rate Analysis\n",
    "\n",
    "To explore whether the non-pity 5-star hit rate in the character pool is affected by the timing of gacha draws, we analyzed over 112,000 non-pity pulls using four different time-based grouping strategies.\n",
    "\n",
    "#### 1. Hourly Grouping (24 segments):\n",
    "Hit rates ranged from 0.39% to 0.92%, with standard errors varying depending on sample size. Although some fluctuations are visually notable (e.g., hour 1 and hour 5 have higher-than-average rates), the Chi-squared test yielded **p = 0.5580**, suggesting no statistically significant difference. Variance appears mostly attributable to natural sampling fluctuations, especially in low-frequency hours (e.g., hour 5 with only 744 attempts).\n",
    "\n",
    "#### 2. 6-Hour Block Grouping:\n",
    "By aggregating into 4 large time blocks (0–5h, 6–11h, etc.), each with 24,000+ attempts, we reduced sample imbalance and improved stability. All blocks showed very similar hit rates (~0.61–0.65%). The Chi-squared test confirmed extremely weak variation (**p = 0.9414**), strongly supporting the hypothesis of time-invariant drop rates.\n",
    "\n",
    "#### 3. 10-Minute Grouping Within Each Hour:\n",
    "Finer granularity revealed hit rates ranging from 0.56% to 0.74%. The Chi-squared test produced **p = 0.2168**, indicating no statistical significance. However, this grouping had the smallest p-value among all methods, suggesting a slight—but still inconclusive—tendency toward temporal variance at finer scales.\n",
    "\n",
    "#### 4. Second-Digit of Second (0–9):\n",
    "Grouping by the last digit of the second at the moment of draw yielded near-uniform hit rates (~0.57–0.69%) and minimal visual fluctuation. The p-value of **0.9390** reinforces the conclusion that no systematic pattern exists at such fine resolution.\n",
    "\n",
    "### Conclusion:\n",
    "Across all grouping strategies, no statistically significant correlation was found between draw timing and non-pity 5-star hit rate. The observed variations are consistent with expected randomness under a stable probability model. This provides empirical support for the game's claim of time-invariant gacha mechanics. While some groupings (especially 10-minute intervals) exhibited minor deviations, these did not reach statistical significance.\n",
    "\n",
    "Thus, the common belief that certain “lucky hours” or “magic seconds” increase draw success remains unconfirmed under rigorous analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b545d2-6786-48b1-abdb-d97e7a0d2507",
   "metadata": {},
   "source": [
    "### Deeper Reflection: When Non-Significant Results Still Matter\n",
    "\n",
    "Although the hourly grouping (24 segments) showed a non-significant Chi-squared result (**p = 0.5580**), the hit rates themselves visibly fluctuated — from 0.39% (hour 8) to 0.92% (hour 1), nearly a 2.4x spread. Some low-sample hours like 5 AM had as few as 744 attempts, yielding standard errors as high as 0.0029 — nearly 50% of the global hit rate.\n",
    "\n",
    "This highlights a critical caveat: **non-significant p-values do not equate to high confidence in the null hypothesis**. Rather, in low-sample regimes, the statistical power may be too weak to detect real differences, even if they exist.\n",
    "\n",
    "Thus, the absence of statistical significance should not be interpreted as strong evidence of \"no temporal bias\" — especially when some hourly bins contribute wide confidence intervals. This motivates a more principled approach to ensure the reliability of statistical conclusions.\n",
    "### Motivation for Sample Size Thresholding via Confidence Intervals\n",
    "\n",
    "To address unstable estimates in low-frequency time bins, we introduce a **minimum sample threshold** based on the width of confidence intervals.\n",
    "\n",
    "The standard error for a binomial proportion is given by:\n",
    "\n",
    "$$\n",
    "SE = \\sqrt{ \\frac{p (1 - p)}{n} }\n",
    "$$\n",
    "\n",
    "Letting $p = 0.006$ (the global non-pity rate) and targeting a margin of error of ±0.0015 (roughly 25% of $p$), we solve:\n",
    "\n",
    "$$\n",
    "0.0015 = \\sqrt{ \\frac{0.006 \\cdot 0.994}{n} } \\Rightarrow n \\approx 5000\n",
    "$$\n",
    "\n",
    "Hence, each time bin should ideally contain **at least 5000 draws** to ensure the estimated hit rate lies within a ±0.0015 margin at 95% confidence.\n",
    "\n",
    "This threshold becomes a guiding principle for subsequent analysis: small-sample bins (e.g., 3 AM or 5 AM) should either be aggregated or excluded to ensure the reliability and interpretability of statistical results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598d5919-05c0-40e0-bfe9-84c9183665ee",
   "metadata": {},
   "source": [
    "### Going Beyond Chi-Squared: Why We Perform a Permutation Test\n",
    "\n",
    "Although our chi-squared tests showed no statistically significant differences in hit rates across time bins, we are not yet fully confident that these observed variations are \"typical\" under the null hypothesis (i.e., draw time is irrelevant). After all, chi-squared tests rely on large-sample approximations and can lose power in unbalanced groups.\n",
    "\n",
    "To address this concern, we conducted a **Permutation Test**, which is a non-parametric resampling-based method. Here's how it works:\n",
    "\n",
    "- We preserve the number of samples per time group (e.g., 24 groups for hours), but **randomly shuffle** the \"hit\" and \"miss\" labels among all draws.\n",
    "- For each reshuffled dataset, we compute the hit rate in each group and calculate its standard deviation.\n",
    "- By repeating this process (e.g., 200 times), we obtain a **null distribution** of hit rate variance under the assumption that timing has no effect.\n",
    "- We then compare the **observed variance** (from real data) with the simulated null distribution to obtain a **p-value**: the fraction of permutations that produced a variance as large as or larger than the observed one.\n",
    "\n",
    "### Why This Matters\n",
    "\n",
    "This test helps us answer: \"Even if the overall hit rates don't differ significantly between time groups, are the **fluctuations** we see still compatible with random chance?\"\n",
    "\n",
    "If the observed variance lies in the **extreme tail** of the simulated distribution, it suggests that the fluctuation we see is unlikely under the null — even if the chi-squared test missed it.\n",
    "\n",
    "Thus, the permutation test complements classical methods by evaluating **distribution-level deviations**, rather than pointwise discrepancies.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b48699c-d528-409b-b7b1-6628baf0f9a2",
   "metadata": {},
   "source": [
    "### Permutation Test with Visualization\n",
    "\n",
    "To further investigate whether the observed variance in 5-star hit rates across different time groups is statistically meaningful, we use a **permutation test** and complement it with a **visual explanation**.\n",
    "\n",
    "The function `permutation_test_by_group_with_plot()` performs the following steps:\n",
    "\n",
    "1. **Calculate actual group-wise hit rate variance**:\n",
    "   - For each time group (e.g., by hour), calculate the hit rate (5-star probability).\n",
    "   - Compute the **standard deviation** of these hit rates as the observed statistic.\n",
    "\n",
    "2. **Generate permutation-based null distribution**:\n",
    "   - Randomly shuffle the \"hit\"/\"non-hit\" labels among all draws (keeping group sizes intact).\n",
    "   - Repeat this process (200 times) and calculate the standard deviation of group hit rates for each shuffled case.\n",
    "   - Store these values to form a **distribution of expected variances under the assumption that draw time has no effect**.\n",
    "\n",
    "3. **Compute p-value**:\n",
    "   - Compare the real observed variance with the permutation distribution.\n",
    "   - The p-value represents the proportion of permutations where the variance is **as large or larger** than observed.\n",
    "   - A low p-value (< 0.05) suggests that observed variance is unusually high, indicating possible time-dependent effects.\n",
    "\n",
    "4. **Visualize the permutation distribution**:\n",
    "   - A histogram shows how standard deviations are distributed under the null hypothesis.\n",
    "   - A **red dashed line** marks the actual observed standard deviation.\n",
    "   - If the red line lies within the bulk of the histogram, the variation is likely due to random noise.\n",
    "   - If the red line lies far to the right, it suggests that real data is more variable than expected by chance.\n",
    "\n",
    "This visual tool helps interpret the statistical test by showing how \"special\" our observed variation is compared to what we'd expect from random draws.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09924712-1efa-48f4-9f28-188646462ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def permutation_test_by_group_with_plot(group_key, group_name):\n",
    "    # Step 1: Calculate the actual standard deviation of hit rates across groups\n",
    "    grouped_attempts = defaultdict(list)\n",
    "    for entry in all_non_pity_attempts:\n",
    "        grouped_attempts[entry[group_key]].append(1 if entry['star'] == 5 else 0)\n",
    "\n",
    "    group_rates = [np.mean(grouped_attempts[g]) for g in sorted(grouped_attempts)]\n",
    "    observed_std = np.std(group_rates)\n",
    "\n",
    "    # Step 2: Construct null distribution using permutation\n",
    "    stars = [entry['star'] == 5 for entry in all_non_pity_attempts]\n",
    "    group_labels = [entry[group_key] for entry in all_non_pity_attempts]\n",
    "\n",
    "    perm_stats = []\n",
    "    for _ in range(200):\n",
    "        shuffled = random.sample(stars, len(stars))\n",
    "        temp_group = defaultdict(list)\n",
    "        for label, value in zip(group_labels, shuffled):\n",
    "            temp_group[label].append(value)\n",
    "        rates = [np.mean(temp_group[g]) for g in sorted(temp_group)]\n",
    "        perm_stats.append(np.std(rates))\n",
    "\n",
    "    # Step 3: Calculate p-value\n",
    "    p_val = np.mean(np.array(perm_stats) >= observed_std)\n",
    "\n",
    "    # Step 4: Print result\n",
    "    print(f\"\\n=== Permutation Test (grouped by {group_name}) ===\")\n",
    "    print(f\"Observed std of hit rates: {observed_std:.6f}\")\n",
    "    print(f\"Proportion of permuted std >= observed std (p-value): {p_val:.4f}\")\n",
    "    if p_val < 0.05:\n",
    "        print(\"Significant: Differences in hit rate may not be due to random variation.\")\n",
    "    else:\n",
    "        print(\"Not significant: Differences in hit rate may be due to natural variation.\")\n",
    "\n",
    "    # Step 5: Visualize permutation distribution\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.hist(perm_stats, bins=30, color='skyblue', edgecolor='black')\n",
    "    plt.axvline(observed_std, color='red', linestyle='--', linewidth=2,\n",
    "                label=f'Observed std = {observed_std:.6f}')\n",
    "    plt.title(f'Permutation Distribution of Std. Dev.\\n({group_name})')\n",
    "    plt.xlabel('Standard Deviation of Hit Rates')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb397219-34dd-442f-804b-d184372c3d89",
   "metadata": {},
   "source": [
    "### Positional Interpretation of Observed Variance within Permutation Distributions\n",
    "\n",
    "From the permutation test histograms, we further assess the position of the observed standard deviation (red dashed line) relative to the permutation-based null distributions:\n",
    "\n",
    "- **10-Minute Grouping**: The observed standard deviation lies slightly **right of the center**, suggesting that real hit rate fluctuations are marginally larger than what randomness alone would predict. Although not statistically significant, this subtle shift warrants attention in future large-scale studies.\n",
    "\n",
    "- **Hour Grouping and Second-Digit Grouping**: In both cases, the observed standard deviation is clearly **left of center**, meaning that the real data is **even more uniform** than expected from random permutations. This reinforces the interpretation that there is no time-related bias — the system may be fairer than chance in these resolutions.\n",
    "\n",
    "This visual analysis enriches the permutation p-values by showing how the real-world variation compares to simulated randomness, and confirms that **only the 10-minute granularity shows mild deviation**, while others support the null hypothesis more strongly.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc7d6ab4-567e-4fce-a279-cf72e01fb015",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Permutation Test (grouped by Hour) ===\n",
      "Observed std of hit rates: 0.001307\n",
      "Proportion of permuted std >= observed std (p-value): 0.6850\n",
      "Not significant: Differences in hit rate may be due to natural variation.\n"
     ]
    },
    {
     "data": {
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lSzFkyBCYmZmhSZMmOg8DGjx4ML788ksMGTIEV65cQcuWLXHo0CHMmTMHoaGhePnll/Vah+TkZBw9ehRqtRp3797V/Gjd1atXsWjRIgQHB5e47JkzZzB+/Hj07dsXXl5eMDc3x2+//YYzZ87gvffe09r+TZs2YfPmzWjQoAEsLS0lN43m5uZYtGgRsrKy0LZtWxw5cgSffPIJunTpovmwWXROztq1a9GwYUP4+Pjgf//7n6bpeJQxPjaTJk3Ct99+i1deeQWzZ8+Gu7s7fv75Zyxfvhxjx45F48aNyx3zwIEDmDBhAgYMGAA/Pz/UqVMH6enp2LhxI3799VcMHjxY86NwJT3n69SpgylTpuCTTz7BiBEj0LdvX1y7dg3h4eE6D3MyNTVFQEBAsXNJdFGr1ZrfkcjNzUVycjJ++eUXbNmyBd7e3tiyZYtmrIeHB2bPno0PPvgAf//9Nzp37oxatWrh5s2b+N///gdra2vMmjVLM75p06bo2LEj5s6di2vXrmH16tVa637ppZcQExNT6lXLiEgCw537TUSVVdHVnP74448njl27dq1o3769sLa2FiqVSjRs2FAMHjxYHD9+XDMmICBANG/evNiyJV2JqOgKQD/88MMT88rNzRXTpk0Tbm5uQqVSiYCAAHH69OliV3MS4uFVojw9PYWJiYnWVYLOnTsngoKChK2trahVq5bo27evSE5O1nmlqOnTpwsXFxehVCq1rpTz+BWDhBDi1q1bYsyYMcLZ2VmYmpoKd3d3MX36dPHgwQOtcQDEm2++Waw+urahpBoW3UxMTEStWrVE69atxcSJE8XZs2eLLfP4FZZu3rwphg4dKpo2bSqsra2FjY2NeOaZZ8SSJUtEQUGBZrkrV66I4OBgYWtrKwAId3d3rXiPP1661iXEf1f9OXPmjAgMDBQqlUrUrl1bjB07VmRlZWktf/fuXTFixAjh6OgorK2tRdeuXcWVK1cqxWMjhBBXr14VYWFhok6dOsLMzEw0adJELFy4UOuqREXxynI1p2vXrokZM2aI5557Tjg5OQlTU1Nha2sr2rdvL7744gutx0uIkp/zarVazJ07V7i5uQlzc3PxzDPPiF27dumsFcp4haeiq3QV3VQqlahfv77o2rWrWLt2rcjNzdW53Pbt20WnTp2EnZ2dsLCwEO7u7qJPnz5i3759xcauXr1aE/vRq7oJ8d9V44hIvxRCPOEyHkRERERERDrwnAkiIiIiIpKEzQQREREREUnCZoKIiIiIiCRhM0FERERERJKwmSAiIiIiIknYTBARUbnNnj0bzZo1g1qtBvDwNx/Gjx+vc+yPP/4IhUKBgwcPPsUM/3P79m3UrFmz2I/QERFRxbGZICKicrlx4wYWLFiA2bNnQ6k0/reRWrVqYdKkSZg6dWqJvw5NRETSGP+7ABERGZWlS5eiZs2a6NWrl6FTKZUQAjk5OQCAMWPG4MqVK/jxxx8NnBURUdXCZoKIiMosLy8Pa9asQVhYWIX3SuzcuRMdO3aElZUVbG1tERQUhLi4OK0xQ4cOhYeHR7Flw8PDoVAotKYVHWq1cuVKeHt7w8LCAt988w0AwNHREUFBQVi5cmWFciYiIm1sJoiIqMyOHTuGW7duoVOnTsXmCSFQUFBQ7FZ0XsWjNmzYgO7du8POzg4bN27EmjVrcPv2bQQGBuLQoUOS89u+fTtWrFiBDz/8EHv27MELL7ygmRcYGIjDhw/jzp07kuMTEZE2U0MnQERElUfRnoNWrVoVm7d8+XIsX778iTHUajWmTp2Kli1b4pdfftHs4QgNDUXDhg3x7rvv4vDhw5Lyy8rKwp9//olatWoVm9eqVSuo1WocPXoUnTt3lhSfiIi0sZkgIqIyu3HjBhQKBezt7YvN69evH6ZOnVps+m+//YZ3331Xc//ChQu4ceMGJk6cqHWolI2NDXr37o1Vq1YhOzsbVlZW5c7vxRdf1NlIAICDgwMAICUlpdxxiYhINzYTRERUZjk5OTAzM4OJiUmxeXXr1kWbNm2KTb9y5YrW/Vu3bgEAnJ2di411cXGBWq3G7du3JTUTumIWsbS0BADNSdlERFRxPGeCiIjKzN7eHnl5ebh//77kGHXq1AEApKamFpt348YNKJVKzd4FS0tL5ObmFhuXkZGhM/bjJ2U/6t9//wUAnXtViIhIGjYTRERUZk2bNgUAXL58WXKMJk2awNXVFRs2bIAQQjP9/v372Lp1q+YKTwDg4eGB9PR03Lx5UzMuLy8Pe/bsKfd6//77bwBAs2bNJOdORETa2EwQEVGZBQYGAgCOHj0qOYZSqcSCBQtw+vRpvPrqq9i5cyd++OEHdOrUCXfu3MG8efM0Y/v37w8TExO89tpr2L17N7Zt24bg4GAUFhaWe71Hjx5FnTp10LJlS8m5ExGRNjYTRERUZm5ubnjhhRewY8eOCsUJCwvD9u3bcevWLfTv3x/Dhg2DnZ0dDhw4gOeff14zztPTEzt27MCdO3fQp08fTJ06FX379sXgwYPLtT4hBHbu3ImwsLBSD4UiIqLyUYhH9zETERE9wdatW9G/f39cvXoVrq6uhk6nTPbv34/g4GCcPXtWc6gWERFVHJsJIiIqFyEE/Pz80Lp1ayxbtszQ6ZRJp06d0KhRI3z11VeGToWIqErhYU5ERFQuCoUCX331leYyrsbu9u3bCAgIwKeffmroVIiIqhzumSAiIiIiIkm4Z4KIiIiIiCRhM0FERERERJKwmSAiIiIiIklMDZ2AMVKr1bhx4wZsbW15PXIiIiIiqhaEELh37x5cXFygVJZtnwObCR1u3LgBNzc3Q6dBRERERPTUXbt2DfXq1SvTWDYTOtja2gJ4WEg7OzsDZ1P55OfnY+/evQgODoaZmZmh06kyKm1dmzYFUlMBZ2fg/HlDZ1NMpa2rkWNd5cPayoN1lQfrKg+56pqZmQk3NzfNZ+GyYDOhQ9GhTXZ2dmwmJMjPz4eVlRXs7Oz4wqFHlbauRbtJlUrACP+eKm1djRzrKh/WVh6sqzxYV3nIXdfyHObPE7CJiIiIiEgSNhNERERERCQJmwkiIiIiIpKE50wQkbwCAoCMDMDe3tCZEJGBCCFQUFCAwsJCQ6dSovz8fJiamuLBgwdGnWdlw7rKQ2pdTUxMYGpqqtefPmAzQUTy+v57Q2dARAaUl5eH1NRUZGdnGzqVUgkh4OTkhGvXrvE3pvSIdZVHRepqZWUFZ2dnmJub6yUXNhNEREQkC7VajaSkJJiYmMDFxQXm5uZG+4FSrVYjKysLNjY2Zf6xLnoy1lUeUuoqhEBeXh7++ecfJCUlwcvLSy+PCZsJIiIikkVeXh7UajXc3NxgZWVl6HRKpVarkZeXB0tLS37o1SPWVR5S66pSqWBmZoarV69qlq8oPqpEREQkK36IJDIe+v575F83EcnrxReB5s0f/ktERERVCpsJIpLXxYvAuXMP/yUiqoI8PDwQERFh6DT05mltj0KhwPbt22VfD8mLzQQRERGRDteuXcMbb7yhOXnc3d0dEyZMwK1btwydmtEKDw/Hs88+a+g0NLZu3YpmzZrBwsICzZo1Q1RU1BOX+fPPPxEQEACVSgVXV1fMnj0bQgitMTExMWjdujUsLS3RoEEDrFy5Umv+2bNn0bt3b3h4eEChUOhszlasWIFnnnkGdnZ2sLOzQ8eOHfHLL79ojRFCIDw8HC4uLlCpVAgMDMTZs2c1869cuQKFQqHz9sMPP5SjUtKxmSAiIiJ6zN9//402bdrg4sWL2LhxIy5duoSVK1di//796NixI/7991+D5VZYWAi1Wm2w9VcWcXFx6N+/PwYNGoT4+HgMGjQI/fr1w7Fjx0pcJjMzE0FBQXBxccEff/yBL774Ap999hkWL16sGZOUlITQ0FC88MILOHXqFN5//328/fbb2Lp1q2ZMdnY2GjRogHnz5sHJyUnnuurVq4d58+bh+PHjOH78OF588UV0795dq1lYsGABFi9ejGXLluGPP/6Ak5MTgoKCcO/ePQCAm5sbUlNTtW6zZs2CtbU1unTpUtESlo2gYu7evSsAiLt37xo6lUopLy9PbN++XeTl5Rk6lSql0tbV1VUI4OG/RqjS1tXIsa7yqUy1zcnJEefOnRM5OTmGTuWJCgsLxe3bt0VhYaEQQojOnTuLevXqiezsbK1xqampwsrKSowZM0Yzzd3dXcyePVu8/vrrwtraWjg7O4vPP/9ca7mPPvpIuLm5CXNzc+Hs7Czeeustzbzc3FwxdepU4eLiIqysrES7du3EgQMHNPMjIyNFjRo1xK5du4S3t7cwMTERK1euFBYWFuL27dta63nrrbeEv7+/5v7hw4fFCy+8ICwtLUW9evXEW2+9JbKysjTzb968KV599VVhaWkpPDw8xPr164W7u7tYsmRJibU6cOCAaNu2rbCyshI1atQQfn5+4sqVKyIyMlIA0LqtWbNG3L59W5w/f1688MILwsLCQnh7e4u9e/cKACIqKupJD41k/fr1E507d9aaFhISIl577bUSl1m+fLmoUaOGePDggWba3LlzhYuLi1Cr1UIIIaZNmyaaNm2qtdzo0aNFhw4ddMZ8Uj0fVatWLfH1118LIYRQq9XCyclJzJs3TzP/wYMHokaNGmL58uVaz9dHPfvss2L48OElrqO0v0spn4G5Z4KIiIjoEf/++y/27NmDcePGQaVSac1zcnLCgAEDsHnzZq1DXxYuXIhnnnkGJ0+exPTp0zFp0iRER0cDAH788UcsWbIEq1atQmJiIrZv346WLVtqlh02bBgOHz6MTZs24cyZM+jbty86d+6MxMREzZjs7GzMnTsXX3/9Nc6ePYuBAweiZs2aWt+GFxYWYsuWLRgwYACAh4frhISEoFevXjhz5gw2b96MQ4cOYfz48Zplhg4diitXruC3337Djz/+iOXLlyM9Pb3E2hQUFKBHjx4ICAjAmTNnEBcXh1GjRkGhUKB///5455130Lx5c8235P3794darUafPn1gYmKCo0ePYuXKlXj33Xef+DjMmTMHNjY2pd5+//33EpePi4tDcHCw1rSQkBAcOXKk1GUCAgJgYWGhtcyNGzdw5cqVUuMeP34c+fn5T9wuXQoLC7Fp0ybcv38fHTt2BPBwD0haWprWuiwsLBAQEIC4uDidcU6cOIHTp0/jjTfekJSHFPydCSKiaiI5ORkZGRl6jWlvb4/69evrNSZVE4sXP7w9SatWwM6d2tO6dQNOnnzyspMnP7yVU2JiIoQQ8Pb21jnf29sbt2/fxj///AMHBwcAwHPPPYf33nsPANC4cWMcPnwYS5YsQVBQEJKTk+Hk5ISXX34ZZmZmqF+/Ptq1awcAuHz5MjZu3Ijr16/DxcUFADBlyhT8+uuviIyMxJw5cwAA+fn5WL58OXx8fDR59O/fHxs2bNB8cNy/fz9u376Nvn37AnjY4ISFhWHixIkAAC8vL3z++ecICAjAihUrkJycjF9++QVHjx5F+/btAQBr1qwpcbuBh4cB3b17F6+++ioaNmyoqUcRGxsbmJqaag7tUavV2LNnDxISEnDlyhXUq1cPwMNG4UmH4YwZMwb9+vUrdYyrq2uJ89LS0uDo6Kg1zdHREWlpaaUu4+HhUWyZonmenp4lxi0oKEBGRgacnZ1LzflRf/75Jzp27IgHDx7AxsYGUVFRaNasmWZ9j67/0XUVNTaPK3r8/Pz8ypxDRbGZICKqBpKTk9HU2xs52dl6jauyssL5hAQ2FFR+mZlASsqTx7m5FZ/2zz9lWzYzs/x5lUHRHolHf8276NvkR+8XnXTbt29fREREoEGDBujcuTNCQ0PRtWtXmJqa4uTJkxBCoHHjxlrL5+bmok6dOpr75ubmeOaZZ7TGDBgwAB07dsSNGzfg4uKC77//HqGhoahVqxaAh99SX7p0Cd9//71W7kW/TH7x4kWYmpqiTZs2mvlNmzZFzZo1S9z22rVrY+jQoQgJCUFQUBBefvll9OvXr9QP0BcvXkT9+vU1jYSuepW0rtq1az9xXGke/8V1IcQTf4Vd1zKPTy/LmLJo0qQJTp8+jTt37mDr1q0YMmQIYmJiNA1FebYhJycHGzZswMyZM8uVQ0WxmSAiqgYyMjKQk52Nfp+sgIOnl15ipiclYsuMscjIyGAzQeVnZweU8q2yRt26uqeVZVk7u/LnBaBRo0ZQKBQ4d+4cevToUWz++fPnUatWLdjb25cap+gDn5ubGy5cuIDo6Gjs27cP48aNw8KFCxETEwO1Wg0TExOcOHECJiYmWsvb2Nho/q9SqYp9gGzXrh0aNmyITZs2YezYsYiKikJkZKRmvlqtxujRo/H2228Xy61+/fq4cOGCVp5lFRkZibfffhu//vorNm/ejBkzZiA6OhodOnTQOV48diWksq5zzpw5mj0zJfnll1/wwgsv6Jzn5ORUbC9Eenp6sW/6y7IM8N8egpLGmJqaajWAZWFubo5GjRoBANq0aYM//vgDS5cuxapVqzR7d9LS0rSatZK24ccff0R2djYGDx5crhwqis0EEVE14uDpBVdvnycPJJKbxEOQABQ/7EnP6tSpg6CgICxfvhyTJk3SOm8iLS0N33//PQYPHqz1gfjo0aNaMY4ePYqmTZtq7qtUKnTr1g3dunXDm2++iaZNm+LPP/+Er68vCgsLkZ6eXuKH4tKEhYXh+++/R7169aBUKvHKK69o5rVq1Qpnz57VfFh9nLe3NwoKCnD8+HHNYVcXLlzAnTt3nrheX19f+Pr6Yvr06ejYsSM2bNiADh06wNzcHIWFhVpjmzRpguTkZM0eFAAlHvP/qIoe5tSxY0dER0dj0qRJmml79+4t9RCgjh074v3330deXh7Mzc01y7i4uGgOf+rYsSN27dqltdzevXvRpk0bmJmZPWmzSiWEQG5uLgDA09MTTk5OiI6Ohq+vLwAgLy8PMTExmDt3brFl16xZg27duqGurgZcRmwmiEheH34IZGUBj3zDRkRk7JYtWwY/Pz+EhITgk08+gaenJ86ePYupU6fC1dUVn376qdb4w4cPY8GCBejRoweio6Pxww8/4OeffwYArFu3DoWFhWjfvj2srKzw3XffQaVSwd3dHXXq1MGAAQMwePBgLFq0CL6+vsjIyMBvv/2Gli1bIjQ0tNQ8BwwYgFmzZuHTTz9Fnz59YGlpqZn37rvvokOHDnjzzTcxcuRIWFtbIyEhAdHR0fjiiy/QpEkTdO7cGSNHjsTq1athamqKiRMnFjvp/FFJSUlYvXo1unXrBhcXF1y4cAEXL17UfBvu4eGBpKQknD59GvXq1YO1tTUCAwPRpEkTzTZmZmbigw8+eOJjUNHDnCZMmAB/f3/Mnz8f3bt3x44dO7Bv3z4cOnRIM2bZsmWIiorC/v37ATxszmbNmoWhQ4fi/fffR2JiIubMmYMPP/xQ0zyOGTMGy5Ytw+TJkzFy5EjExcVhzZo12LhxoyZuXl4ezp07p/l/SkoKTp8+DRsbG01z9/7776NLly5wc3PDvXv3sGnTJhw8eBC//vorgId7byZOnIg5c+bAy8sLXl5emDNnDqysrBAWFqa1x+fSpUuIjY3F7t27JddLKl7NiYjkNWrUw28fR40ydCZERGXm5eWF48ePo2HDhujfvz8aNmyIUaNGoVOnToiLiyv2Ifedd97BiRMn4Ovri48//hiLFi1CSEgIAKBmzZr46quv8Nxzz+GZZ57B/v37sWvXLs0hMZGRkRg8eDDeeecdNGnSBN26dcOxY8fgput8ER15tm3bFmfOnNFcxanIM888g5iYGCQmJuKFF16Ar68vZs6cqXXITGRkJNzc3BAQEIBevXph1KhRmpPKdbGyssL58+fRu3dvNG7cGKNGjcL48eMxevRoAEDv3r3RuXNndOrUCXXr1sXGjRuhVCqxdetW5Obmol27dhgxYkSxZkwOfn5+2LRpEyIjI/HMM89g3bp12Lx5s+Zkc+DhIaCXL1/W3K9Rowaio6Nx/fp1tGnTBuPGjcPkyZMx+ZG9aJ6enti9ezcOHjyIZ599Fh9//DE+//xz9O7dWzPmxo0bmr03qamp+Oyzz+Dr64sRI0Zoxty8eRODBg1CkyZN8NJLL+HYsWP49ddfERQUpBkzbdo0TJw4EePGjUObNm2QkpKCvXv3wtbWVmtb165dC1dX12JXmXoaFELXgWzVXGZmJmrUqIG7d+/CTuLxltVZfn4+du/ejdDQ0Arv7qP/sK7yqC51PXnyJFq3bo3x3+/T22FOKQnxWDbgZZw4cQKtWrXSmldd6moIlam2Dx48QFJSEjw9PbW+MTdGarUamZmZsLOzg1LJ71r1hXWVR0XqWtrfpZTPwHxUiYiIiIhIEp4zQUTySk0FCgsBExOgHNfeJiIiIuPHPRNEJK+2bR9eJ75tW0NnQkRERHrGZoKIiIiIiCRhM0FERERERJKwmSAiIiJZ8cKRRMZD33+PbCaIiIhIFkWXrs3OzjZwJkRUpOjvUV+Xljbo1Zzmzp2Lbdu24fz581CpVPDz88P8+fPRpEkTzRghBGbNmoXVq1fj9u3baN++Pb788ks0b9681Nhbt27FzJkzcfnyZTRs2BCffvopevbsKfcmERER0f8zMTFBzZo1kZ6eDuDhD54V/YqwsVGr1cjLy8ODBw/4ewh6xLrKQ0pdhRDIzs5Geno6atasCRMTE73kYtBmIiYmBm+++Sbatm2LgoICfPDBBwgODsa5c+dgbW0NAFiwYAEWL16MdevWoXHjxvjkk08QFBSECxcuFPv1vyJxcXHo378/Pv74Y/Ts2RNRUVHo168fDh06pPWrh0RERCQvJycnANA0FMZKCIGcnByoVCqjbXgqI9ZVHhWpa82aNTV/l/pg0Gbi119/1bofGRkJBwcHnDhxAv7+/hBCICIiAh988AF69eoFAPjmm2/g6OiIDRs2aH66/XEREREICgrC9OnTAQDTp09HTEwMIiIisHHjRnk3ioiIiDQUCgWcnZ3h4OCA/Px8Q6dTovz8fMTGxsLf39/of1m8MmFd5SG1rmZmZnrbI1HEqH607u7duwCA2rVrAwCSkpKQlpaG4OBgzRgLCwsEBATgyJEjJTYTcXFxmDRpkta0kJAQREREyJM4ERERlcrExETvH2L0ycTEBAUFBbC0tOSHXj1iXeVhTHU1mmZCCIHJkyfj+eefR4sWLQAAaWlpAABHR0etsY6Ojrh69WqJsdLS0nQuUxTvcbm5ucjNzdXcz8zMBPCw6zPmb1GMVVHNWDv9qqx1NQWgACAAFBhh7pW1ruWlVquhUqlgAgGlukAvMU0goFKpoFari9WvutTVEFhbebCu8mBd5SFXXaXEM5pmYvz48Thz5gwOHTpUbN7jx4IJIZ54fFh5lpk7dy5mzZpVbPrevXthZWX1pNSpBNHR0YZOoUqqbHW1ef99KAoLIUxMkLV7t6HTKVFlq6sUDw/zvA9cP6aXeE2sgU4bNyIlJQUpKSk6x1SHuhoKaysP1lUerKs89F1XKVdeM4pm4q233sLOnTsRGxuLevXqaaYXnRySlpYGZ2dnzfT09PRiex4e5eTkVGwvRGnLTJ8+HZMnT9bcz8zMhJubG4KDg2FnZydpm6qz/Px8REdHIygoyOC73qoS1lUe1aWu8fHx8Pf3x6ivd8KlSQu9xLxx4S+sHtENsbGx8PHx0ZpXXepqCKytPFhXebCu8pCrrkVH55SHQZsJIQTeeustREVF4eDBg/D09NSa7+npCScnJ0RHR8PX1xcAkJeXh5iYGMyfP7/EuB07dkR0dLTWeRN79+6Fn5+fzvEWFhawsLAoNt3MzIxP/Apg/eTBusqjqtdVqVQiJycHhVBArdTPS38hFMjJyYFSqSyxdlW9robE2sqDdZUH6yoPfddVSiyDNhNvvvkmNmzYgB07dsDW1lazN6FGjRqaS11NnDgRc+bMgZeXF7y8vDBnzhxYWVkhLCxME2fw4MFwdXXF3LlzAQATJkyAv78/5s+fj+7du2PHjh3Yt2+fzkOoiIiIiIhIGoM2EytWrAAABAYGak2PjIzE0KFDAQDTpk1DTk4Oxo0bp/nRur1792r9xkRycrLWD3b4+flh06ZNmDFjBmbOnImGDRti8+bN/I0JIkPYsAHIzgasrIBHvgQgIiKiys/ghzk9iUKhQHh4OMLDw0scc/DgwWLT+vTpgz59+lQgOyLSi2nTgJQUwNWVzQQREVEVw981JyIiIiIiSdhMEBERERGRJGwmiIiIiIhIEjYTREREREQkCZsJIiIiIiKShM0EERERERFJwmaCiIiIiIgkYTNBRERERESSGPRH64ioGnBy0v6XiIiIqgw2E0Qkr+PHDZ0BERERyYSHORERERERkSRsJoiIiIiISBI2E0REREREJAnPmSAieY0eDfz7L1C7NrBqlaGzISIiIj1iM0FE8vr5ZyAlBXB1NXQmREREpGc8zImIiIiIiCRhM0FERERERJKwmSAiIiIiIknYTBARERERkSRsJoiIiIiISBI2E0REREREJAmbCSIiIiIikoTNBBERERERScIfrSMieb3+OnD7NlCrlqEzISIiIj1jM0FE8lq40NAZEBERkUx4mBMREREREUnCZoKIiIiIiCRhM0FERERERJKwmSAieTVtCtjZPfyXiIiIqhQ2E0Qkr6ws4N69h/8SERFRlcJmgoiIiIiIJGEzQUREREREkrCZICIiIiIiSdhMEBERERGRJAZtJmJjY9G1a1e4uLhAoVBg+/btWvMVCoXO28JSflF33bp1Opd58OCBzFtDRERERFS9GLSZuH//Pnx8fLBs2TKd81NTU7Vua9euhUKhQO/evUuNa2dnV2xZS0tLOTaBiIiIiKjaMjXkyrt06YIuXbqUON/JyUnr/o4dO9CpUyc0aNCg1LgKhaLYskREREREpF8GbSbK4+bNm/j555/xzTffPHFsVlYW3N3dUVhYiGeffRYff/wxfH19Sxyfm5uL3Nxczf3MzEwAQH5+PvLz8yuefDVTVDPWTr8qa11NASgACAAFRph7Za1reanVaqhUKphAQKku0EtMEwioVCqo1epi9asudTUE1lYerKs8WFd5yFVXKfEUQgih1ywkUigUiIqKQo8ePXTOX7BgAebNm4cbN26UesjS0aNHcenSJbRs2RKZmZlYunQpdu/ejfj4eHh5eelcJjw8HLNmzSo2fcOGDbCyspK0PUT0kOMff8AkLw+F5ua42batodMhIiKiEmRnZyMsLAx3796FnZ1dmZapNM1E06ZNERQUhC+++KJccdVqNVq1agV/f398/vnnOsfo2jPh5uaGjIyMMheS/pOfn4/o6GgEBQXBzMzM0OlUGayrPKpLXePj4+Hv749RX++ES5MWeol548JfWD2iG2JjY+Hj46M1r7rU1RBYW3mwrvJgXeUhV10zMzNhb29frmaiUhzm9Pvvv+PChQvYvHlzuZdVKpVo27YtEhMTSxxjYWEBCwuLYtPNzMz4xK8A1k8erKs8qnpdlUolcnJyUAgF1Er9vPQXQoGcnBwolcoSa1fV62pIrK08WFd5sK7y0HddpcSqFL8zsWbNGrRu3brYN19lIYTA6dOn4ezsLENmRERERETVl0H3TGRlZeHSpUua+0lJSTh9+jRq166N+vXrA3i4u+WHH37AokWLdMYYPHgwXF1dMXfuXADArFmz0KFDB3h5eSEzMxOff/45Tp8+jS+//FL+DSKi4k6cAPLyAHNzoHVrQ2dDREREemTQZuL48ePo1KmT5v7kyZMBAEOGDMG6desAAJs2bYIQAq+//rrOGMnJyVAq/9vBcufOHYwaNQppaWmoUaMGfH19ERsbi3bt2sm3IURUsu7dgZQUwNUVuH7d0NkQERGRHhm0mQgMDMSTzv8eNWoURo0aVeL8gwcPat1fsmQJlixZoo/0iIiIiIioFJXinAkiIiIiIjI+bCaIiIiIiEgSNhNERERERCQJmwkiIiIiIpKEzQQREREREUnCZoKIiIiIiCRhM0FERERERJKwmSAiIiIiIkkM+qN1RFQNJCQAQgAKhaEzISIiIj1jM0FE8rK1NXQGREREJBMe5kRERERERJKwmSAiIiIiIkl4mBMRyWvxYiAzE7CzAyZPNnQ2REREpEdsJohIXosXAykpgKsrmwkiIqIqhoc5ERERERGRJGwmiIiIiIhIEjYTREREREQkCZsJIiIiIiKShM0EERERERFJwmaCiIiIiIgkYTNBRERERESSsJkgIiIiIiJJ+KN1RCSvVq0ANzegbl1DZ0JERER6xmaCiOS1c6ehMyAiIiKZ8DAnIiIiIiKShM0EERERERFJwmaCiIiIiIgk4TkTRCSvbt2Af/55eAI2z58gIiKqUthMEJG8Tp4EUlIAV1dDZ0JERER6xsOciIiIiIhIEjYTREREREQkCZsJIiIiIiKShM0EERERERFJYtBmIjY2Fl27doWLiwsUCgW2b9+uNX/o0KFQKBRatw4dOjwx7tatW9GsWTNYWFigWbNmiIqKkmkLiIiIiIiqL4M2E/fv34ePjw+WLVtW4pjOnTsjNTVVc9u9e3epMePi4tC/f38MGjQI8fHxGDRoEPr164djx47pO30iIiIiomrNoJeG7dKlC7p06VLqGAsLCzg5OZU5ZkREBIKCgjB9+nQAwPTp0xETE4OIiAhs3LixQvkSEREREdF/jP53Jg4ePAgHBwfUrFkTAQEB+PTTT+Hg4FDi+Li4OEyaNElrWkhICCIiIkpcJjc3F7m5uZr7mZmZAID8/Hzk5+dXbAOqoaKasXb6pY+6Xr9+Hbdu3dJXSgCAOnXqoF69eiXONwWgACAAFBjhc6K6PF/VajVUKhVMIKBUF+glpgkEVCoV1Gp1sfpVl7oaAmsrD9ZVHqyrPOSqq5R4CiGE0GsWEikUCkRFRaFHjx6aaZs3b4aNjQ3c3d2RlJSEmTNnoqCgACdOnICFhYXOOObm5li3bh3CwsI00zZs2IBhw4ZpNQyPCg8Px6xZs4pN37BhA6ysrCq2YUTVXMMdO2CanY0CKytc7t7d0OkQERFRCbKzsxEWFoa7d+/Czs6uTMsY9Z6J/v37a/7fokULtGnTBu7u7vj555/Rq1evEpdTKBRa94UQxaY9avr06Zg8ebLmfmZmJtzc3BAcHFzmQtJ/8vPzER0djaCgIJiZmRk6nSqjonWNj4+Hv78/es5cgrruDfWS0z9XLyPq40mIjY2Fj4+P7kGhoZr/NtHLWvWrujxfix7/UV/vhEuTFnqJeePCX1g9opvOx7+61NUQWFt5sK7yYF3lIVddi47OKQ+jbiYe5+zsDHd3dyQmJpY4xsnJCWlpaVrT0tPT4ejoWOIyFhYWOvd0mJmZ8YlfAayfPKTWValUIicnB7XdG8HJu4QP/uVUCAVycnKgVCor/WNd1Z+vRY9/IRRQK/Xz0l+Wx7+q19WQWFt5sK7yYF3loe+6Svp8obe1PwW3bt3CtWvX4OzsXOKYjh07Ijo6Wmva3r174efnJ3d6RERERETVikH3TGRlZeHSpUua+0lJSTh9+jRq166N2rVrIzw8HL1794azszOuXLmC999/H/b29ujZs6dmmcGDB8PV1RVz584FAEyYMAH+/v6YP38+unfvjh07dmDfvn04dOjQU98+IgJw7x4gBKBQALa2hs6GiIiI9MigeyaOHz8OX19f+Pr6AgAmT54MX19ffPjhhzAxMcGff/6J7t27o3HjxhgyZAgaN26MuLg42D7ygSQ5ORmpqama+35+fti0aRMiIyPxzDPPYN26ddi8eTPat2//1LePiAB4ewM1ajz8l4iIiKoUg+6ZCAwMRGkXk9qzZ88TYxw8eLDYtD59+qBPnz4VSY2IiIiIiJ6gUp0zQURERERExoPNBBERERERScJmgoiIiIiIJGEzQUREREREkrCZICIiIiIiSdhMEBERERGRJGwmiIiIiIhIEjYTREREREQkiUF/tI6IqoEdO4C8PMDc3NCZEBERkZ6xmSAiebVubegMiIiISCZsJoiMUHJyMjIyMrSmqdVqAEB8fDyUyvIfoZiQkKCX3IiIiIiKsJkgMjLJyclo6u2NnOxsrekqlQobN26Ev78/cnJyDJQdERER0X/YTBAZmYyMDORkZ6PfJyvg4OmlmW4CAeA+Rn29E4VQlDvuhcP7Eb18rh4zLaOffgJycgCVCnj11ae/fiIiIpINmwkiI+Xg6QVXbx/NfaW6ALh+DC5NWkCtLP+fbnpSoj7TK7sxY4CUFMDVFbh+3TA5EBERkSx4aVgiIiIiIpKEzQQREREREUnCZoKIiIiIiCRhM0FERERERJKwmSAiIiIiIknYTBARERERkSRsJoiIiIiISBI2E0REREREJAmbCSKSl40NYGv78F8iIiKqUvgL2EQkr/PnDZ0BERERyYR7JoiIiIiISBI2E0REREREJAmbCSIiIiIikoTnTBCRvKZOBW7fBmrVAhYuLNMiycnJyMjI0Hsq9vb2qF+/vt7jEhERVVdsJohIXhs3AikpgKtrmZqJ5ORkNPX2Rk52tt5TUVlZ4XxCAhsKIiIiPWEzQURGJSMjAznZ2ej3yQo4eHrpLW56UiK2zBiLjIwMNhNERER6IqmZSEpKgqenp75zISLScPD0gqu3j6HTICIiolJIOgG7UaNG6NSpE9avX48HDx7oOyciIiIiIqoEJDUT8fHx8PX1xTvvvAMnJyeMHj0a//vf//SdGxERERERGTFJzUSLFi2wePFipKSkIDIyEmlpaXj++efRvHlzLF68GP/880+Z4sTGxqJr165wcXGBQqHA9u3bNfPy8/Px7rvvomXLlrC2toaLiwsGDx6MGzdulBpz3bp1UCgUxW7cg0JEREREpF8V+p0JU1NT9OzZE1u2bMH8+fNx+fJlTJkyBfXq1cPgwYORmppa6vL379+Hj48Pli1bVmxednY2Tp48iZkzZ+LkyZPYtm0bLl68iG7duj0xLzs7O6SmpmrdLC0tJW8nEREREREVV6GrOR0/fhxr167Fpk2bYG1tjSlTpuCNN97AjRs38OGHH6J79+6lHv7UpUsXdOnSRee8GjVqIDo6WmvaF198gXbt2iE5ObnUq7EoFAo4OTlJ2ygiIiIiIioTSc3E4sWLERkZiQsXLiA0NBTffvstQkNDoVQ+3NHh6emJVatWoWnTpnpN9u7du1AoFKhZs2ap47KysuDu7o7CwkI8++yz+Pjjj+Hr61vi+NzcXOTm5mruZ2ZmAnh4qFV+fr5ecq9OimrG2kmjVquhUqlgAgGlukAzvej/j04rD1OlQmfcijCBgEqlglqtLvHxNgWgACAAFJThOVHS9suVa3V5vspR19Ie/+pSV0NgbeXBusqDdZWHXHWVEk8hhBDlXcjLywvDhw/HsGHDStwDkJeXh40bN2LIkCFlS0ShQFRUFHr06KFz/oMHD/D888+jadOmWL9+fYlxjh49ikuXLqFly5bIzMzE0qVLsXv3bsTHx8PLS/c168PDwzFr1qxi0zds2AArK6sy5U9EuvksXw6zrCzk29ggftw4Q6dDREREJcjOzkZYWBju3r0LOzu7Mi0jqZmQQ2nNRH5+Pvr27Yvk5GQcPHiwzBsHPPw2rlWrVvD398fnn3+uc4yuPRNubm7IyMgo17roofz8fERHRyMoKAhmZmaGTqfSiY+Ph7+/P0Z9vRMuTVpopivVBfC6cQKJLq2hVpZ/p2L83h2I+nhSsbgVcePCX1g9ohtiY2Ph46Of34QoafsrqqRcq8vzVY66lvb4V5e6GgJrKw/WVR6sqzzkqmtmZibs7e3L1UxIOswpMjISNjY26Nu3r9b0H374AdnZ2WXeG1EW+fn56NevH5KSkvDbb7+V+8O9UqlE27ZtkZiYWOIYCwsLWFhYFJtuZmbGJ34FsH7SKJVK5OTkoBAKnU2DWmkqqZkoUItS40pRCAVycnKgVCr19lg/afulelKuVf35Kkddy/L4V/W6GhJrKw/WVR6sqzz0XVcpsSRdzWnevHmwt7cvNt3BwQFz5syRElKnokYiMTER+/btQ506dcodQwiB06dPw9nZWW95ERERERGRxD0TV69ehaenZ7Hp7u7uSE5OLnOcrKwsXLp0SXM/KSkJp0+fRu3ateHi4oI+ffrg5MmT+Omnn1BYWIi0tDQAQO3atWFubg4AGDx4MFxdXTF37lwAwKxZs9ChQwd4eXkhMzMTn3/+OU6fPo0vv/xSyqYSEREREVEJJDUTDg4OOHPmDDw8PLSmx8fHl2vvwfHjx9GpUyfN/cmTJwMAhgwZgvDwcOzcuRMA8Oyzz2otd+DAAQQGBgIAkpOTNVeRAoA7d+5g1KhRSEtLQ40aNeDr64vY2Fi0a9euHFtIRHrTpg2QlgY4OQHHjxs6GyIiItIjSc3Ea6+9hrfffhu2trbw9/cHAMTExGDChAl47bXXyhwnMDAQpZ3/XZZzww8ePKh1f8mSJViyZEmZcyAimaWlASkphs6CiIiIZCCpmfjkk09w9epVvPTSSzA1fRhCrVZj8ODBej1ngoiIiIiIjJekZsLc3BybN2/Gxx9/jPj4eKhUKrRs2RLu7u76zo+ISK8SEhK07qvVagAPD9N89JDJsrK3t0f9+vX1khsREVFlU6HrAzZu3BiNGzfWVy5ERLK5l3ETCqUSAwcO1JquUqmwceNG+Pv7Iycnp9xxVVZWOJ+QwIaCiIiqJUnNRGFhIdatW4f9+/cjPT1d881ekd9++00vyRER6UvOvUwItRr9PlkBB08vzXQTCAD3MerrnSiEolwx05MSsWXGWGRkZLCZICKiaklSMzFhwgSsW7cOr7zyClq0aAGFonxvwEREhuLg6QVX7/9+rVmpLgCuH4NLkxZ6/ZE8IiKi6kDSO+emTZuwZcsWhIaG6jsfIiIiIiKqJCT9Ara5uTkaNWqk71yIiIiIiKgSkdRMvPPOO1i6dGmZfgeCiIiIiIiqJkmHOR06dAgHDhzAL7/8gubNm8PMzExr/rZt2/SSHBFVAQsWANnZgJWVoTMhIiIiPZPUTNSsWRM9e/bUdy5EVBWFhRk6AyIiIpKJpGYiMjJS33kQEREREVElI+mcCQAoKCjAvn37sGrVKty7dw8AcOPGDWRlZektOSIiIiIiMl6S9kxcvXoVnTt3RnJyMnJzcxEUFARbW1ssWLAADx48wMqVK/WdJxFVVhcuAAUFgKkp0KSJobMhIiIiPZK0Z2LChAlo06YNbt++DZVKpZnes2dP7N+/X2/JEVEV8NJLQIsWD/8lIiKiKkXy1ZwOHz4Mc3Nzrenu7u5ISUnRS2JERERERGTcJO2ZUKvVKCwsLDb9+vXrsLW1rXBSRERERERk/CQ1E0FBQYiIiNDcVygUyMrKwkcffYTQ0FB95UZEREREREZM0mFOS5YsQadOndCsWTM8ePAAYWFhSExMhL29PTZu3KjvHImIiIiIyAhJaiZcXFxw+vRpbNy4ESdPnoRarcYbb7yBAQMGaJ2QTUREREREVZekZgIAVCoVhg8fjuHDh+szH6JKJzk5GRkZGXqLl5CQoLdYVDnp+zkF8HklFzkeK3t7e9SvX1+vMYmI5CKpmfj2229LnT948GBJyRBVNsnJyWjq7Y2c7GxDp0JVBJ9TlYdcj5XKygrnExLYUBBRpSCpmZgwYYLW/fz8fGRnZ8Pc3BxWVlZsJqjayMjIQE52Nvp9sgIOnl56iXnh8H5EL5+rl1hU+cjxnAL4vJKDHI9VelIitswYi4yMDDYTRFQpSGombt++XWxaYmIixo4di6lTp1Y4KaLKxsHTC67ePnqJlZ6UqJc4VLnp8zkF8HklJ30/VkRElYnkcyYe5+XlhXnz5mHgwIE4f/68vsISUWX3xx9AYSFgYmLoTIiIiEjP9NZMAICJiQlu3Lihz5BEVNk5Oxs6AyIiIpKJpGZi586dWveFEEhNTcWyZcvw3HPP6SUxIiIiIiIybpKaiR49emjdVygUqFu3Ll588UUsWrRIH3kREREREZGRk9RMqNVqfedBRFXV6tVAVhZgYwOMGmXobIiIiEiP9HrOBBFRMbNnAykpgKsrmwkiIqIqRlIzMXny5DKPXbx4sZRVEBERERGRkZPUTJw6dQonT55EQUEBmjRpAgC4ePEiTExM0KpVK804hUKhnyyJiIiIiMjoSGomunbtCltbW3zzzTeoVasWgIc/ZDds2DC88MILeOedd/SaJBERERERGR+llIUWLVqEuXPnahoJAKhVqxY++eQTXs2JiIiIiKiakNRMZGZm4ubNm8Wmp6en4969e2WOExsbi65du8LFxQUKhQLbt2/Xmi+EQHh4OFxcXKBSqRAYGIizZ88+Me7WrVvRrFkzWFhYoFmzZoiKiipzTkREREREVDaSmomePXti2LBh+PHHH3H9+nVcv34dP/74I9544w306tWrzHHu378PHx8fLFu2TOf8BQsWYPHixVi2bBn++OMPODk5ISgoqNSGJS4uDv3798egQYMQHx+PQYMGoV+/fjh27Fi5t5OIiIiIiEom6ZyJlStXYsqUKRg4cCDy8/MfBjI1xRtvvIGFCxeWOU6XLl3QpUsXnfOEEIiIiMAHH3ygaVC++eYbODo6YsOGDRg9erTO5SIiIhAUFITp06cDAKZPn46YmBhERERg48aN5dlMIiIiIiIqhaRmwsrKCsuXL8fChQtx+fJlCCHQqFEjWFtb6y2xpKQkpKWlITg4WDPNwsICAQEBOHLkSInNRFxcHCZNmqQ1LSQkBBERESWuKzc3F7m5uZr7mZmZAID8/HxNs0RlV1Sz6lA7tVoNlUoFEwgo1QV6iWmqVOiMWfR/qespKW5FmEBApVJBrVaX+HibAlAAEAAKyvCckKOmgDx1Ldr+hIQEvf2Y54ULF57q9ldEadtfdP/UqVNQKsu3E7xOnTqoV6+eXnKUkxzP1bL8TVWn19iniXWVB+sqD7nqKiWeQgghpK7w0qVLuHz5Mvz9/aFSqSCEkHw5WIVCgaioKPTo0QMAcOTIETz33HNISUmBi4uLZtyoUaNw9epV7NmzR2ccc3NzrFu3DmFhYZppGzZswLBhw7QahkeFh4dj1qxZxaZv2LABVlZWkraHiB7ymzkTFnfuILdmTRz5+GNDp0NEREQlyM7ORlhYGO7evQs7O7syLSNpz8StW7fQr18/HDhwAAqFAomJiWjQoAFGjBiBmjVr6vWKTo83J2VpWMq7zPTp07V+iC8zMxNubm4IDg4ucyHpP/n5+YiOjkZQUBDMzMwMnY6s4uPj4e/vj1Ff74RLkxb6ibl3B6I+nlQsplJdAK8bJ5Do0hpqZfn/dEuKWxE3LvyF1SO6ITY2Fj4+ProHhYYCAFQAQsuSpww1BeSpa1HMnjOXoK57Q73kmXg0Bge+XvTUtl8fMXVtvwkE/K2zEXvfCoUo+5dM/1y9jKiPJ5X+nDIScjxXy/I3VZ1eY58m1lUerKs85Kpr0dE55SGpmZg0aRLMzMyQnJwMb29vzfT+/ftj0qRJemkmnJycAABpaWlwdnbWTE9PT4ejo2Opy6WlpWlNe9IyFhYWsLCwKDbdzMyMT/wKqA71UyqVyMnJQSEUkj7g61KgFqXGVCtNJa3rSXGlKIQCOTk5UCqVenus5agpIE9di2LWdm8EJ2/9fPBNTbpkkO2vSExd269UFwDXj8GxSctyrU+O55Rc5Hiulmf7q8NrrCGwrvJgXeWh77pKiSXpak579+7F/Pnzix3T6uXlhatXr0oJWYynpyecnJwQHR2tmZaXl4eYmBj4+fmVuFzHjh21linKt7RliIiIiIio/CR9lXL//n2d5xJkZGTo/Ia/JFlZWbh06ZLmflJSEk6fPo3atWujfv36mDhxIubMmQMvLy94eXlhzpw5sLKy0jofYvDgwXB1dcXcuXMBABMmTIC/vz/mz5+P7t27Y8eOHdi3bx8OHTokZVOJiIiIiKgEkpoJf39/fPvtt/j4/0+mVCgUUKvVWLhwITp16lTmOMePH9caX3TewpAhQ7Bu3TpMmzYNOTk5GDduHG7fvo327dtj7969sLW11SyTnJysdaUQPz8/bNq0CTNmzMDMmTPRsGFDbN68Ge3bt5eyqURUUQMGABkZgL098P33hs6GiIiI9EhSM7Fw4UIEBgbi+PHjyMvLw7Rp03D27Fn8+++/OHz4cJnjBAYGorSLSSkUCoSHhyM8PLzEMQcPHiw2rU+fPujTp0+Z8yAiGcXEACkpgKuroTMhIiIiPZN0zkSzZs1w5swZtGvXDkFBQbh//z569eqFU6dOoWFD/VzRhIiIiIiIjFu590zk5+cjODgYq1at0vnbDEREREREVD2Ue8+EmZkZ/vrrL8k/TkdERERERFWDpMOcBg8ejDVr1ug7FyIiIiIiqkQknYCdl5eHr7/+GtHR0WjTpg2sra215i9evFgvyRERERERkfEqVzPx999/w8PDA3/99RdatWoFALh48aLWGB7+RERERERUPZSrmfDy8kJqaioOHDgAAOjfvz8+//xzODo6ypIcEREREREZr3KdM/H4b0L88ssvuH//vl4TIiIiIiKiykHSORNFSvvBOSIiAMDIkcDdu0CNGobOhIiIiPSsXM2EQqEodk4Ez5EgolJ99JGhMyAiIiKZlKuZEEJg6NChsLCwAAA8ePAAY8aMKXY1p23btukvQyIyegkJCUYZi+hRycnJyMjI0Fs8OZ+rpcVWq9UAgPj4eCiVZT9a2d7eHvXr169wbkREjypXMzFkyBCt+wMHDtRrMkRUudzLuAmFUsnXAjJ6ycnJaOrtjZzsbEOnUqqy/E2pVCps3LgR/v7+yMnJKXNslZUVzicksKEgIr0qVzMRGRkpVx5EVAnl3MuEUKvR75MVcPD00kvMC4f3I3r5XL3EIiqSkZGBnOxso3+uluVvygQCwH2M+nonClG2Q43TkxKxZcZYZGRksJkgIr2q0AnYREQA4ODpBVdvH53zxnV+Bnbpqch0cMbyX888MVZ6UqK+0yPSKO25Wl5yPldLy1OpLgCuH4NLkxZQK/k2TkSGVa5LwxIRERERERVhM0FERERERJKwmSAiIiIiIknYTBARERERkSRsJoiIiIiISBI2E0REREREJAmbCSIiIiIikoTNBBERERERScJmgoiIiIiIJOFPZxKRrH76ZAVM8nJRaG5h6FSIiIhIz9hMEJGskts8Z+gUiIiISCY8zImIiIiIiCRhM0FERERERJLwMCciklX944c150zwkCciIqKqhc0EEcnq1RljYZeeikwHZyz/9Yyh0yEiIiI94mFOREREREQkCZsJIiIiIiKShM0EERERERFJwmaCiIiIiIgkMfpmwsPDAwqFotjtzTff1Dn+4MGDOsefP3/+KWdORERERFS1Gf3VnP744w8UFhZq7v/1118ICgpC3759S13uwoULsLOz09yvW7eubDkSEREREVVHRt9MPN4EzJs3Dw0bNkRAQECpyzk4OKBmzZoyZkZEREREVL0ZfTPxqLy8PKxfvx6TJ0+GQqEodayvry8ePHiAZs2aYcaMGejUqVOJY3Nzc5Gbm6u5n5mZCQDIz89Hfn6+fpKvRopqVh1qp1aroVKpYAIBpbpALzFNlQqdMYv+L3U9JcWVI9dHKR75tyzrlSPP0uJWpK6GqqmxxC0tptS6mkBApVJBrVbr9TXkaf6tyh1TSm3lqmtVUp3eu54m1lUectVVSjyFEELoNQsZbdmyBWFhYUhOToaLi4vOMRcuXEBsbCxat26N3NxcfPfdd1i5ciUOHjwIf39/ncuEh4dj1qxZxaZv2LABVlZWet0Gouom+I03oLp1Czl16mDvmjWGToeIiIhKkJ2djbCwMNy9e1frdIHSVKpmIiQkBObm5ti1a1e5luvatSsUCgV27typc76uPRNubm7IyMgocyHpP/n5+YiOjkZQUBDMzMwMnY6s4uPj4e/vj1Ff74RLkxb6ibl3B6I+nlQsplJdAK8bJ5Do0hpqZfl3KpYUV45cjS1maXErUteqsP1yxZRa1xsX/sLqEd0QGxsLHx8fveQJPN2/VbljSqmtXHWtSqrTe9fTxLrKQ666ZmZmwt7evlzNRKU5zOnq1avYt28ftm3bVu5lO3TogPXr15c438LCAhYWFsWmm5mZ8YlfAdWhfkqlEjk5OSiEQtIHfF0K1KLUmGqlqaR1PSmuFJUlZlniSqlrVdp+uWKWt66FUCAnJwdKpVKvrx+G+FuVO2Z5aitXXaui6vDeZQisqzz0XVcpsYz+0rBFIiMj4eDggFdeeaXcy546dQrOzs4yZEVEREREVH1Vij0TarUakZGRGDJkCExNtVOePn06UlJS8O233wIAIiIi4OHhgebNm2tO2N66dSu2bt1qiNSJiIiIiKqsStFM7Nu3D8nJyRg+fHixeampqUhOTtbcz8vLw5QpU5CSkgKVSoXmzZvj559/Rmho6NNMmYj+33OrFsIiKxO5NnY4PHqqodMhIiIiPaoUzURwcDBKOk983bp1WvenTZuGadOmPYWsiKgsfKK+g116KjIdnNlMEBERVTGV5pwJIiIiIiIyLpVizwRVP8nJycjIyNB7XHt7e9SvX1/vcYmIiIiqIzYTZHSSk5PR1NsbOdnZeo+tsrLC+YQENhREREREesBmgoxORkYGcrKz0e+TFXDw9NJb3PSkRGyZMRYZGRlsJoiIiIj0gM0EGS0HTy+4evOXWomIiIiMFU/AJiIiIiIiSdhMEBERERGRJGwmiIiIiIhIEp4zQUSyutbaD6rbt5BTq46hUyEiIiI9YzNBRLLa9elKQ6dAREREMuFhTkREREREJAmbCSIiIiIikoTNBBERERERScJzJohIVq+P6gmrf/9Bdu262Lg6ytDpEBERkR6xmSAiWdVKvgy79FRkZmUaOhUiIiLSMx7mREREREREkrCZICIiIiIiSdhMEBERERGRJGwmiIiIiIhIEjYTREREREQkCZsJIiIiIiKShM0EERERERFJwmaCiIiIiIgk4Y/WEZGsDo+cAvOc+8hTWRs6FSIiItIzNhNEJKv43oMNnQIRERHJhIc5ERERERGRJGwmiIiIiIhIEh7mRESysv4nDUq1GmqlEvfrOhk6HSIiItIjNhNEJKshg4Jhl56KTAdnLP/1jKHTISIiIj3iYU5ERERERCQJmwkiIiIiIpKEzQQREREREUnCZoKIiIiIiCQx6mYiPDwcCoVC6+bkVPrVYGJiYtC6dWtYWlqiQYMGWLly5VPKloiIiIioejH6qzk1b94c+/bt09w3MTEpcWxSUhJCQ0MxcuRIrF+/HocPH8a4ceNQt25d9O7d+2mkS0RERERUbRh9M2FqavrEvRFFVq5cifr16yMiIgIA4O3tjePHj+Ozzz5jM0FEREREpGdGfZgTACQmJsLFxQWenp547bXX8Pfff5c4Ni4uDsHBwVrTQkJCcPz4ceTn58udKhERERFRtWLUeybat2+Pb7/9Fo0bN8bNmzfxySefwM/PD2fPnkWdOnWKjU9LS4Ojo6PWNEdHRxQUFCAjIwPOzs4615Obm4vc3FzN/czMTABAfn4+m5AyuH79Om7duqW5r1arAQCnTp2CUln+fvXChQtQqVQwgYBSXaC3PE0goFKpoFar9fa4qtVqvedqqlTojFn0f6nrKSmuHLk+SvHIv2VZrxx5lha3InU1VE2NJW5pMaXWtejvNCEhQfNaog9yvK4Y6vGXUls5Xv+qmqK6sD76xbrKQ666SomnEEIIvWYho/v376Nhw4aYNm0aJk+eXGx+48aNMWzYMEyfPl0z7fDhw3j++eeRmppa4uFS4eHhmDVrVrHpGzZsgJWVlf42gKgasklJgaKwEMLEBFmuroZOh4iIiEqQnZ2NsLAw3L17F3Z2dmVaxqj3TDzO2toaLVu2RGJios75Tk5OSEtL05qWnp4OU1NTnXsyikyfPl2rOcnMzISbmxuCg4PLXMjqKj4+Hv7+/ug5cwnqujcE8PAbMH/rbMTet0Kh5nvpsks8GoMDXy/CqK93wqVJC73leuPCX1g9ohtiY2Ph4+Ojl5hF26/PXOP37kDUx5OKxVSqC+B14wQSXVpDrSz/n25JceXIVUs9GWJKIEddDVZTI4lbWkypdS2K+ehrij7I8bpiqMdfSm3leP2ravLz8xEdHY2goCCYmZkZOp0qg3WVh1x1LTo6pzwqVTORm5uLhIQEvPDCCzrnd+zYEbt27dKatnfvXrRp06bUQltYWMDCwqLYdDMzMz7xn0CpVCInJwe13RvByfvhG5RSXQBcPwbHJi0lfehNTbqEnJwcFEIhafmSFEKBnJwcKJVKvT2uRduvz1wL1KLUmGqlqaR1PSmuFJUlZlniSqlrVdp+uWKWt65FMR99TdEHOV5XDP34l6e2crz+VVV875cH6yoPfddVSiyjPgF7ypQpiImJQVJSEo4dO4Y+ffogMzMTQ4YMAfBwj8LgwYM148eMGYOrV69i8uTJSEhIwNq1a7FmzRpMmTLFUJtARERERFRlGfWeievXr+P1119HRkYG6tatiw4dOuDo0aNwd3cHAKSmpiI5OVkz3tPTE7t378akSZPw5ZdfwsXFBZ9//jkvC0tkQM1+2QrTB9kosLTCuS78WyQiIqpKjLqZ2LRpU6nz161bV2xaQEAATp48KVNGRFRegUtnwS49FZkOzmwmiIiIqhijPsyJiIiIiIiMF5sJIiIiIiKShM0EERERERFJwmaCiIiIiIgkYTNBRERERESSsJkgIiIiIiJJ2EwQEREREZEkbCaIiIiIiEgSo/7ROiKq/O7XcdD6l4iIiKoONhNEJKtvvt9n6BSIiIhIJmwmqNpJSEgwylhERHLT92uWvb096tevr9eYRFS5sJmgauNexk0olEoMHDjQ0KkQET1Vcr3+qayscD4hgQ0FUTXGZoKqjZx7mRBqNfp9sgIOnl56iXnh8H5EL5+rl1hERHKR4/UvPSkRW2aMRUZGBpsJomqMzQRVOw6eXnD19tFLrPSkRL3EqcpCPnkHqszbyLGrhT0zFhk6HaJqTZ+vf0REAJsJIpJZw0PRsEtPRaaDs6FTISIiIj3j70wQEREREZEkbCaIiIiIiEgSNhNERERERCQJmwkiIiIiIpKEzQQREREREUnCZoKIiIiIiCRhM0FERERERJKwmSAiIiIiIkn4o3VEJKuEkF6wvHcHD2xrGjoVIiIi0jM2E0QkqwOTwg2dAhEREcmEhzkREREREZEkbCaIiIiIiEgSNhNERERERCQJz5kwUsnJycjIyNB7XHt7e9SvX1/vcYlKMrJXR9j8k4asuk74alucodMhokpAjvfA3NxcWFhYlDhfrVYDAOLj46FUlu27Vr6nErGZMErJyclo6u2NnOxsvcdWWVnhfEICX/zoqTHLvg+L+1nItb5v6FSIqBKQ6z1QoVRC/H/DoItKpcLGjRvh7++PnJycMsXkeyoRmwmjlJGRgZzsbPT7ZAUcPL30Fjc9KRFbZoxFRkYGX/iIiMgoyfEeeOHwfkQvn1tqTBMIAPcx6uudKITiiTH5nkr0EJsJI+bg6QVXbx9Dp0FERPTU6fM9MD0p8YkxleoC4PoxuDRpAbWSH4+IyoonYBMRERERkSRG3UzMnTsXbdu2ha2tLRwcHNCjRw9cuHCh1GUOHjwIhUJR7Hb+/PmnlDURERERUfVg1M1ETEwM3nzzTRw9ehTR0dEoKChAcHAw7t9/8omcFy5cQGpqqubm5aW/cw+IiIiIiMjIz5n49ddfte5HRkbCwcEBJ06cgL+/f6nLOjg4oGbNmjJmR0RERERUvRn1nonH3b17FwBQu3btJ4719fWFs7MzXnrpJRw4cEDu1IiIiIiIqh2j3jPxKCEEJk+ejOeffx4tWrQocZyzszNWr16N1q1bIzc3F9999x1eeuklHDx4sMS9Gbm5ucjNzdXcz8zMBADk5+cjPz9fvxtSBmq1GiqVCiYQD68uoScmEFCpVFCr1XrbLl25Pv5veZkqFbJsvxxxn2ZMY6xrWWIqHvm3LOt92o9/RepaWZ5TcsUtLabUulaV7ZczppTaypGrHO8pgDzvgXLUVa7tr2qKasMa6ZdcdZUSTyGEEHrNQiZvvvkmfv75Zxw6dAj16tUr17Jdu3aFQqHAzp07dc4PDw/HrFmzik3fsGEDrKysJOVLRA85/vEHTPLyUGhujptt2xo6HSIiIipBdnY2wsLCcPfuXdjZ2ZVpmUrRTLz11lvYvn07YmNj4enpWe7lP/30U6xfvx4JCQk65+vaM+Hm5oaMjIwyF1Kf4uPj4e/vj1Ff74RLk5L3wpTXjQt/YfWIboiNjYWPj36u3a0rV6W6AF43TiDRpbWka3XH792BqI8n6X375Yj7NGMaY10rS8zS4lakrlVh++WKKbWuVWX75YwppbZy5CrHewogz3ugHHWVa/urmvz8fERHRyMoKAhmZmaGTqfKkKuumZmZsLe3L1czYdSHOQkh8NZbbyEqKgoHDx6U1EgAwKlTp+Ds7FzifAsLC1hYWBSbbmZmZpAnvlKpRE5ODgqh0OsP5xRCgZycHCiVSr1tV2m5qpWmkvIvUAtZtl+OuIaIaUx1rSwxyxJXSl2r0vbLFbO8da1q2y9nzPLUVo5c5XhPAeR5D5SjrnJtf1VlqM9UVZ2+6yolllE3E2+++SY2bNiAHTt2wNbWFmlpaQCAGjVqQKVSAQCmT5+OlJQUfPvttwCAiIgIeHh4oHnz5sjLy8P69euxdetWbN261WDbQURERERUFRl1M7FixQoAQGBgoNb0yMhIDB06FACQmpqK5ORkzby8vDxMmTIFKSkpUKlUaN68OX7++WeEhoY+rbSJ6BGO5+Jhkp+HQjNz3GzGQwGIiIiqEqNuJspyOse6deu07k+bNg3Tpk2TKSMiKq/ekwfBLj0VmQ7OWP7rGUOnQ0RERHpUqX5ngoiIiIiIjIdR75kgIiIiMmYlXSmyInJzc3VeGKYyxlSr1QAeXqVLqSzbd9j29vaoX79+hfOjp4PNBBEREVE53cu4CYVSiYEDB+o9tkKphPj/D+GVPaZKpcLGjRvh7++PnJycMsVVWVnhfEICG4pKgs0EERERUTnl3MuEUKvR75MVcPD00lvcC4f3I3r5XL3GNWRMEwgA9zHq650ohOKJcdOTErFlxlhkZGSwmagk2EwQERERSeTg6QVXb/1dqS49KVHvcQ0ZU6kuAK4fg0uTFnr97RgyHjwBm4iIiIiIJGEzQUREREREkrCZICIiIiIiSdhMEBERERGRJDwThohk9fXWI4AQgOLJV/EgIiKiyoXNBBHJKs/axtApEBERkUx4mBMREREREUnCZoKIiIiIiCThYU5EJKu261fAIusecm1s8cfAsYZOh4iIiPSIzQQRyart+hWwS09FpoMzmwkiIqIqhoc5ERERERGRJGwmiIiIiIhIEjYTREREREQkCZsJIiIiIiKShCdgExERkWQJCQlGHY8IAJKTk5GRkaHXmLm5ubCwsNBrTACwt7dH/fr19R5XLmwmiIiIqNzuZdyEQqnEwIEDDZ0KUamSk5PR1NsbOdnZeo2rUCoh1Gq9xgQAlZUVzickVJqGgs0EERERlVvOvUwItRr9PlkBB08vvcW9cHg/opfP1Vs8ooyMDORkZ+v1uVr0PNX38z89KRFbZoxFRkYGmwkiIiKq+hw8veDq7aO3eOlJiXqLRfQofT5Xi56n+n7+V0ZsJohIVjebPoN7jq7IrlXH0KkQERGRnrGZICJZbY1Yb+gUiIiISCa8NCwREREREUnCZoKIiIiIiCRhM0FERERERJLwnAkiklXviQNhdfsWsmvV4fkTREREVQybCSKSleP5M7BLT0Wmg7OhUyEiIiI942FOREREREQkCZsJIiIiIiKShM0EERERERFJUimaieXLl8PT0xOWlpZo3bo1fv/991LHx8TEoHXr1rC0tESDBg2wcuXKp5QpEREREVH1YfTNxObNmzFx4kR88MEHOHXqFF544QV06dIFycnJOscnJSUhNDQUL7zwAk6dOoX3338fb7/9NrZu3fqUMyciIiIiqtqMvplYvHgx3njjDYwYMQLe3t6IiIiAm5sbVqxYoXP8ypUrUb9+fURERMDb2xsjRozA8OHD8dlnnz3lzImIiIiIqjajbiby8vJw4sQJBAcHa00PDg7GkSNHdC4TFxdXbHxISAiOHz+O/Px82XIlIiIiIqpujPp3JjIyMlBYWAhHR0et6Y6OjkhLS9O5TFpams7xBQUFyMjIgLNz8Wvd5+bmIjc3V3P/7t27AIB///3XIA1IZmYmLC0tcfPCnyjIztJb3FvXkmBpaYkTJ04gMzNTLzETExOL5WoCATfrHCSfOopCKMod8/a1v2XZfjniPs2YxljXssS8k5er+ffqicMGybO0uBWpa2V5TskVt7SYUutaVbZfzphSaltZtl+uuHLUtaptv1wxy1vXp/VZpaLkevyLtj8zMxO3bt0qcVx+fj6ys7Nx69YtmJmZ6W399+7dAwAIIcq+kDBiKSkpAoA4cuSI1vRPPvlENGnSROcyXl5eYs6cOVrTDh06JACI1NRUnct89NFHAgBvvPHGG2+88cYbb7xV+9u1a9fK/HndqPdM2Nvbw8TEpNheiPT09GJ7H4o4OTnpHG9qaoo6deroXGb69OmYPHmy5r5arca///6LOnXqQKEo/zfA1V1mZibc3Nxw7do12NnZGTqdKoN1lQfrKg/WVT6srTxYV3mwrvKQq65CCNy7dw8uLi5lXsaomwlzc3O0bt0a0dHR6Nmzp2Z6dHQ0unfvrnOZjh07YteuXVrT9u7dizZt2pS4G8jCwgIWFhZa02rWrFmx5Al2dnZ84ZAB6yoP1lUerKt8WFt5sK7yYF3lIUdda9SoUa7xRn0CNgBMnjwZX3/9NdauXYuEhARMmjQJycnJGDNmDICHexUGDx6sGT9mzBhcvXoVkydPRkJCAtauXYs1a9ZgypQphtoEIiIiIqIqyaj3TABA//79cevWLcyePRupqalo0aIFdu/eDXd3dwBAamqq1m9OeHp6Yvfu3Zg0aRK+/PJLuLi44PPPP0fv3r0NtQlERERERFWS0TcTADBu3DiMGzdO57x169YVmxYQEICTJ0/KnBWVxMLCAh999FGxQ8eoYlhXebCu8mBd5cPayoN1lQfrKg9jqqtCiPJc+4mIiIiIiOghoz9ngoiIiIiIjBObCSIiIiIikoTNBBERERERScJmoppbvnw5PD09YWlpidatW+P3338vdXxMTAxat24NS0tLNGjQACtXriw2ZuvWrWjWrBksLCzQrFkzREVFac2PjY1F165d4eLiAoVCge3btxeLIYRAeHg4XFxcoFKpEBgYiLNnz1ZoW58mY6xrfn4+3n33XbRs2RLW1tZwcXHB4MGDcePGjQpv79NkjLV93OjRo6FQKBAREVHezTMYY65rQkICunXrhho1asDW1hYdOnTQuoqfMTPWumZlZWH8+PGoV68eVCoVvL29sWLFigpt69NkiLrOnTsXbdu2ha2tLRwcHNCjRw9cuHBBawzfu/RfV753yfucfZTk964y/1Y2VTmbNm0SZmZm4quvvhLnzp0TEyZMENbW1uLq1as6x//999/CyspKTJgwQZw7d0589dVXwszMTPz444+aMUeOHBEmJiZizpw5IiEhQcyZM0eYmpqKo0ePasbs3r1bfPDBB2Lr1q0CgIiKiiq2rnnz5glbW1uxdetW8eeff4r+/fsLZ2dnkZmZqfc66Jux1vXOnTvi5ZdfFps3bxbnz58XcXFxon379qJ169ay1EEOxlrbR0VFRQkfHx/h4uIilixZoq9Nl5Ux1/XSpUuidu3aYurUqeLkyZPi8uXL4qeffhI3b97Uex30zZjrOmLECNGwYUNx4MABkZSUJFatWiVMTEzE9u3b9V4HfTNUXUNCQkRkZKT466+/xOnTp8Urr7wi6tevL7KysjRj+N6l/7ryvUve52yRirx3sZmoxtq1ayfGjBmjNa1p06bivffe0zl+2rRpomnTplrTRo8eLTp06KC5369fP9G5c2etMSEhIeK1117TGVPXG51arRZOTk5i3rx5mmkPHjwQNWrUECtXrnzidhmasdZVl//9738CQIkvaMbG2Gt7/fp14erqKv766y/h7u5eaZoJY65r//79xcCBA8uyGUbHmOvavHlzMXv2bK1prVq1EjNmzChxe4yFMdRVCCHS09MFABETEyOE4HuXEPLUVRe+d+m3thV97+JhTtVUXl4eTpw4geDgYK3pwcHBOHLkiM5l4uLiio0PCQnB8ePHkZ+fX+qYkmLqkpSUhLS0NK04FhYWCAgIKFccQzDmuupy9+5dKBQK1KxZs0JxngZjr61arcagQYMwdepUNG/evFzLGpIx11WtVuPnn39G48aNERISAgcHB7Rv3/6Jh5kZA2OuKwA8//zz2LlzJ1JSUiCEwIEDB3Dx4kWEhISUK87TZkx1vXv3LgCgdu3aAPjeBchT15LG8L1LP7XVx3sXm4lqKiMjA4WFhXB0dNSa7ujoiLS0NJ3LpKWl6RxfUFCAjIyMUseUFLOk9RQtV5E4hmDMdX3cgwcP8N577yEsLAx2dnaS4zwtxl7b+fPnw9TUFG+//Xa5ljM0Y65reno6srKyMG/ePHTu3Bl79+5Fz5490atXL8TExJQ5jiEYc10B4PPPP0ezZs1Qr149mJubo3Pnzli+fDmef/75csV52oylrkIITJ48Gc8//zxatGihiVG0XFnjGAtjruvj+N6l39rq472rUvwCNslHoVBo3RdCFJv2pPGPTy9vTH3lZkyMua7AwxPaXnvtNajVaixfvlxSDEMxxtqeOHECS5cuxcmTJyvNc/RxxlhXtVoNAOjevTsmTZoEAHj22Wdx5MgRrFy5EgEBAWWOZSjGWFfgYTNx9OhR7Ny5E+7u7oiNjcW4cePg7OyMl19+uVyxDMHQdR0/fjzOnDmDQ4cOVTg3Y2LMdQX43vX49IrWVl/vXdwzUU3Z29vDxMSkWAebnp5erNMt4uTkpHO8qakp6tSpU+qYkmKWtB4AFY5jCMZc1yL5+fno168fkpKSEB0dXSm+2QGMu7a///470tPTUb9+fZiamsLU1BRXr17FO++8Aw8PjzLHMQRjrqu9vT1MTU3RrFkzrene3t5GfzUnY65rTk4O3n//fSxevBhdu3bFM888g/Hjx6N///747LPPyhzHEIyhrm+99RZ27tyJAwcOoF69elrrAfjepe+6FuF7l/5rq6/3LjYT1ZS5uTlat26N6OhorenR0dHw8/PTuUzHjh2Ljd+7dy/atGkDMzOzUseUFFMXT09PODk5acXJy8tDTExMueIYgjHXFfjvxTgxMRH79u3TvChVBsZc20GDBuHMmTM4ffq05ubi4oKpU6diz549ZY5jCMZcV3Nzc7Rt27bYpQwvXrwId3f3MscxBGOua35+PvLz86FUan8EMDEx0ewNMlaGrKsQAuPHj8e2bdvw22+/wdPTU2s837vkqSvA9y5Antrq7b2rXKdrU5VSdKmyNWvWiHPnzomJEycKa2trceXKFSGEEO+9954YNGiQZnzRpcomTZokzp07J9asWVPsUmWHDx8WJiYmYt68eSIhIUHMmzev2KXK7t27J06dOiVOnTolAIjFixeLU6dOaV2VYd68eaJGjRpi27Zt4s8//xSvv/56pbu8nrHVNT8/X3Tr1k3Uq1dPnD59WqSmpmpuubm5T6k6FWOstdWlMl3NyZjrum3bNmFmZiZWr14tEhMTxRdffCFMTEzE77///hQqUzHGXNeAgADRvHlzceDAAfH333+LyMhIYWlpKZYvX/4UKlMxhqrr2LFjRY0aNcTBgwe1Xj+zs7M1Y/jepf+68r1L3ufs46S8d7GZqOa+/PJL4e7uLszNzUWrVq20Lhc2ZMgQERAQoDX+4MGDwtfXV5ibmwsPDw+xYsWKYjF/+OEH0aRJE2FmZiaaNm0qtm7dqjX/wIEDAkCx25AhQzRj1Gq1+Oijj4STk5OwsLAQ/v7+4s8//9TrtsvJGOualJSkcz4AceDAAX2XQDbGWFtdKlMzIYRx13XNmjWiUaNGwtLSUvj4+FSK30IoYqx1TU1NFUOHDhUuLi7C0tJSNGnSRCxatEio1Wq9br9cDFHXkl4/IyMjNWP43qX/uvK9S97n7OOkvHcp/n9lRERERERE5cJzJoiIiIiISBI2E0REREREJAmbCSIiIiIikoTNBBERERERScJmgoiIiIiIJGEzQUREREREkrCZICIiIiIiSdhMEBERERGRJGwmiIieoitXrkChUOD06dOVKrYU69atQ82aNY0mjj5s374djRo1gomJCSZOnFihWEOHDkWPHj30khcRkaGwmSCiKi09PR2jR49G/fr1YWFhAScnJ4SEhCAuLk4zRqFQYPv27YZL8ikKDAyEQqGAQqGAhYUFXF1d0bVrV2zbtk3v6+rfvz8uXrxYrmU8PDwQERFR4ThyGT16NPr06YNr167h448/1jlG1zYAQHh4OJ599lnN/aVLl2LdunWa+4GBgWVqUB59DM3NzdGwYUNMnz4dubm55dqWsq6PiKg0poZOgIhITr1790Z+fj6++eYbNGjQADdv3sT+/fvx77//Gjo1yfLy8mBubi55+ZEjR2L27NnIz89HSkoKoqKi8Nprr2Ho0KFYvXq13vJUqVRQqVRGE6eisrKykJ6ejpCQELi4uFQ4Xo0aNSQvW/QY5uXl4Y8//sCwYcMAAHPnzq1wXkRE5cE9E0RUZd25cweHDh3C/Pnz0alTJ7i7u6Ndu3aYPn06XnnlFQAPv0UGgJ49e0KhUGjuX758Gd27d4ejoyNsbGzQtm1b7Nu3Tyu+h4cH5syZg+HDh8PW1hb169cv9mH8f//7H3x9fWFpaYk2bdrg1KlTWvMLCwvxxhtvwNPTEyqVCk2aNMHSpUu1xhQdDjN37ly4uLigcePGZYpdEisrKzg5OcHNzQ0dOnTA/PnzsWrVKnz11Vda25iSkoL+/fujVq1aqFOnDrp3744rV64AAPbs2QNLS0vcuXNHK/bbb7+NgIAAAMUPT3pSTQMDA3H16lVMmjRJ8827rjgAsGLFCjRs2BDm5uZo0qQJvvvuO635CoUCX3/9NXr27AkrKyt4eXlh586dpdbl9u3bGDx4MGrVqgUrKyt06dIFiYmJAICDBw/C1tYWAPDiiy9CoVDg4MGDpcZ7kkcPcxo6dChiYmKwdOlSzbYX1VqXosewfv366N27N4KCgrB3717N/Fu3buH1119HvXr1YGVlhZYtW2Ljxo1a6y5pfefOnUNoaChsbGzg6OiIQYMGISMjQ7Psjz/+iJYtW0KlUqFOnTp4+eWXcf/+/QrVgogqLzYTRFRl2djYwMbGBtu3by/xEJA//vgDABAZGYnU1FTN/aysLISGhmLfvn04deoUQkJC0LVrVyQnJ2stv2jRIs0H+XHjxmHs2LE4f/48AOD+/ft49dVX0aRJE5w4cQLh4eGYMmWK1vJqtRr16tXDli1bcO7cOXz44Yd4//33sWXLFq1x+/fvR0JCAqKjo/HTTz+VKXZ5DBkyBLVq1dIc7pSdnY1OnTrBxsYGsbGxOHToEGxsbNC5c2fk5eXh5ZdfRs2aNbF161ZNjMLCQmzZsgUDBgzQuY4n1XTbtm2oV68eZs+ejdTUVKSmpuqMExUVhQkTJuCdd97BX3/9hdGjR2PYsGE4cOCA1rhZs2ahX79+OHPmDEJDQzFgwIBS90gNHToUx48fx86dOxEXFwchBEJDQ5Gfnw8/Pz9cuHABALB161akpqbCz8+v7AV+gqVLl6Jjx44YOXKkZtvd3NzKtGx8fDwOHz4MMzMzzbQHDx6gdevW+Omnn/DXX39h1KhRGDRoEI4dO1bq+lJTUxEQEIBnn30Wx48fx6+//oqbN2+iX79+AIDU1FS8/vrrGD58OBISEnDw4EH06tULQgi91YKIKhlBRFSF/fjjj6JWrVrC0tJS+Pn5ienTp4v4+HitMQBEVFTUE2M1a9ZMfPHFF5r77u7uYuDAgZr7arVaODg4iBUrVgghhFi1apWoXbu2uH//vmbMihUrBABx6tSpEtczbtw40bt3b839IUOGCEdHR5Gbm6uZJjV2QECAmDBhgs557du3F126dBFCCLFmzRrRpEkToVarNfNzc3OFSqUSe/bsEUII8fbbb4sXX3xRM3/Pnj3C3Nxc/Pvvv0IIISIjI0WNGjVKzEUI3TVdsmSJ1pjH4/j5+YmRI0dqjenbt68IDQ3V3AcgZsyYobmflZUlFAqF+OWXX3TmcfHiRQFAHD58WDMtIyNDqFQqsWXLFiGEELdv3xYAxIEDB0rdJnd3d2Fubi6sra21bmZmZsLHx0czbsiQIaJ79+6a+6U9No8KCAgQZmZmwtraWpibmwsAQqlUih9//LHU5UJDQ8U777xT6vpmzpwpgoODtaZdu3ZNABAXLlwQJ06cEADElStXnpgnEVUP3DNBRFVa7969cePGDezcuRMhISE4ePAgWrVqpXXiqy7379/HtGnT0KxZM9SsWRM2NjY4f/58sT0TzzzzjOb/CoUCTk5OSE9PBwAkJCTAx8cHVlZWmjEdO3Ystq6VK1eiTZs2qFu3LmxsbPDVV18VW0/Lli21zpMoa+zyEEJoDi06ceIELl26BFtbW80entq1a+PBgwe4fPkyAGDAgAE4ePAgbty4AQD4/vvvERoailq1aumMX9aaPklCQgKee+45rWnPPfccEhIStKY9+thYW1vD1tZW89joimlqaor27dtrptWpUwdNmjQpFrcspk6ditOnT2vdxowZU+44JRkwYABOnz6NuLg49OvXD8OHD0fv3r018wsLC/Hpp5/imWeeQZ06dWBjY4O9e/c+sdYnTpzAgQMHNI+5jY0NmjZtCuDhYWo+Pj546aWX0LJlS/Tt2xdfffUVbt++rbftIqLKhydgE1GVZ2lpiaCgIAQFBeHDDz/EiBEj8NFHH2Ho0KElLjN16lTs2bMHn332GRo1agSVSoU+ffogLy9Pa9yjh5YADxsKtVoNAGU69GPLli2YNGkSFi1ahI4dO8LW1hYLFy7UHI5SxNraWut+WWKXR2FhIRITE9G2bVsADw+/at26Nb7//vtiY+vWrQsAaNeuHRo2bIhNmzZh7NixiIqKQmRkZInrKGtNy6Ko6SnyaCNUpLTH5nEl1VNX3LKwt7dHo0aNtKbVrl273HFKUqNGDU389evXo3nz5lizZg3eeOMNAA8Pv1uyZAkiIiLQsmVLWFtbY+LEiU+stVqtRteuXTF//vxi85ydnWFiYoLo6GgcOXIEe/fuxRdffIEPPvgAx44dg6enp962j4gqD+6ZIKJqp1mzZlonjJqZmaGwsFBrzO+//46hQ4eiZ8+eaNmyJZycnEo9Ibak9cTHxyMnJ0cz7ejRo8XW4+fnh3HjxsHX1xeNGjXSfPNf0djl8c033+D27duab7dbtWqFxMREODg4oFGjRlq3R69CFBYWhu+//x67du2CUqnUnNiuS1lqam5uXuyxeJy3tzcOHTqkNe3IkSPw9vYu51b/p1mzZigoKNBq4m7duoWLFy9WKG55lGXbdTEzM8P777+PGTNmIDs7G8DDWnfv3h0DBw6Ej48PGjRooDmZvLT1tWrVCmfPnoWHh0exx72ooVUoFHjuuecwa9YsnDp1Cubm5oiKipK41URU2bGZIKIq69atW3jxxRexfv16nDlzBklJSfjhhx+wYMECdO/eXTPOw8MD+/fvR1pamuaQjUaNGmHbtm04ffo04uPjERYWVuK32iUJCwuDUqnEG2+8gXPnzmH37t347LPPtMY0atQIx48fx549e3Dx4kXMnDlTcxJ4RWOXJDs7G2lpabh+/TqOHTuGd999F2PGjMHYsWPRqVMnAA8Po7G3t0f37t3x+++/IykpCTExMZgwYQKuX7+uiTVgwACcPHkSn376Kfr06QNLS8sS11uWmnp4eCA2NhYpKSlaVxB61NSpU7Fu3TqsXLkSiYmJWLx4MbZt21ahE9C9vLzQvXt3jBw5EocOHUJ8fDwGDhwIV1dXreeKnDw8PHDs2DFcuXIFGRkZ5Xq+hYWFQaFQYPny5QAe1rpoD0JCQgJGjx6NtLS0J67vzTffxL///ovXX38d//vf//D3339j7969GD58OAoLC3Hs2DHMmTMHx48fR3JyMrZt24Z//vnnqTVcRGR82EwQUZVlY2OD9u3bY8mSJfD390eLFi0wc+ZMjBw5EsuWLdOMW7RoEaKjo+Hm5gZfX18AwJIlS1CrVi34+fmha9euCAkJQatWrcq9/l27duHcuXPw9fXFBx98UOzwkTFjxqBXr17o378/2rdvj1u3bmHcuHF6iV2Sr776Cs7OzmjYsCF69uyJc+fOYfPmzZoPosDDS4/Gxsaifv366NWrF7y9vTF8+HDk5OTAzs5OM87Lywtt27bFmTNnSryKU5Gy1HT27Nm4cuUKGjZsqDmc6nE9evTA0qVLsXDhQjRv3hyrVq1CZGQkAgMDy7T9JYmMjETr1q3x6quvomPHjhBCYPfu3cUOl5LLlClTYGJigmbNmqFu3brlOpfE3Nwc48ePx4IFC5CVlYWZM2eiVatWCAkJQWBgIJycnIr92rau9bm4uODw4cMoLCxESEgIWrRogQkTJqBGjRpQKpWws7NDbGwsQkND0bhxY8yYMQOLFi1Cly5d9FwNIqosFELfB94SEREREVG1wD0TREREREQkCZsJIiIiIiKShM0EERERERFJwmaCiIiIiIgkYTNBRERERESSsJkgIiIiIiJJ2EwQEREREZEkbCaIiIiIiEgSNhNERERERCQJmwkiIiIiIpKEzQQREREREUnCZoKIiIiIiCT5P5HxeSXPHOdqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Permutation Test (grouped by 10-Minute Interval) ===\n",
      "Observed std of hit rates: 0.000639\n",
      "Proportion of permuted std >= observed std (p-value): 0.2000\n",
      "Not significant: Differences in hit rate may be due to natural variation.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Permutation Test (grouped by Second Unit Digit) ===\n",
      "Observed std of hit rates: 0.000446\n",
      "Proportion of permuted std >= observed std (p-value): 0.9400\n",
      "Not significant: Differences in hit rate may be due to natural variation.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "permutation_test_by_group_with_plot('hour', 'Hour')\n",
    "permutation_test_by_group_with_plot('minute_group', '10-Minute Interval')\n",
    "permutation_test_by_group_with_plot('second_digit', 'Second Unit Digit')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad7d51f-91a9-48d9-aefe-df1815ef12ba",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
